CN114429549A - Image edge extraction method, image edge extraction device and storage medium - Google Patents

Abstract

The present disclosure relates to an image edge extraction method, an image edge extraction device, and a storage medium. The image edge extraction method comprises the following steps: acquiring an image to be processed, and performing Fourier transform on the image to be processed to obtain frequency spectrum information of the image to be processed; modulating the frequency spectrum information of the image to be processed based on a vortex filter to obtain the modulated frequency spectrum information of the image to be processed, wherein the transmittance function of the vortex filter is obtained by modulating a composite vortex function based on a Bessel function; and carrying out inverse Fourier transform on the modulated frequency spectrum information to obtain an edge image of the image to be processed. By the aid of the method and the device, high-contrast isotropic and anisotropic edge enhancement can be achieved for the image to be processed, and the processing effect of edge extraction is improved.

Description

Image edge extraction method, image edge extraction device and storage medium

technical field

The present disclosure relates to the technical field of image processing, and in particular, to an image edge extraction method, an image edge extraction device and a storage medium.

Background technique

With the rapid development of science and technology, optical information and image processing technology have been further developed, edge is the most basic feature of image, edge detection is an important link in image analysis and recognition, and is the key to dealing with many problems. Other features of the image are derived from the basic features of edges and regions, and all the information in the image can be recovered through edge information. The effect of edge detection will directly affect the performance of image segmentation and recognition. Image edge detection technology plays an important role in computer vision, image analysis and other applications.

There are many types of edge detection algorithms, such as differential operator method, template matching method, wavelet detection method, neural network method and so on. Although there are many edge detection and edge extraction methods in the current technology, the images processed by these methods have problems such as reduced contrast, low edge sharpness, and high background noise, which affect the effect of image edge detection.

SUMMARY OF THE INVENTION

In order to overcome the problems existing in the related art, the present disclosure provides an image edge extraction method, an image edge extraction device and a storage medium.

According to an aspect of the embodiments of the present disclosure, an image edge extraction method is provided, including: acquiring an image to be processed, and performing Fourier transform on the to-be-processed image to obtain spectral information of the to-be-processed image; based on vortex filtering The device modulates the spectral information of the image to be processed to obtain the modulated spectral information of the image to be processed, wherein the transmittance function of the vortex filter is obtained by modulating the composite vortex function based on the Bessel function. ; Perform inverse Fourier transform on the modulated spectrum information to obtain the edge image of the to-be-processed image.

In one embodiment, the transmittance function of the vortex filter is obtained by modulating and superimposing the vortex function based on the Bessel function in the following manner: based on the circular aperture function and the second-order Bessel function of the first type, determine the Bessel function. Searle function; based on the superposition of the positive vortex function and the negative vortex function, a composite vortex function is obtained; based on the Bessel function, the composite vortex function is modulated to obtain the transmittance function of the vortex filter .

In one embodiment, determining the Bessel function based on the circular aperture function and the first-type second-order Bessel function includes: determining the first-type second-order based on the vortex filter radius and the edge enhancement amplitude value modulation parameter Bessel function, and determine the circular aperture function based on the radius of the vortex filter and the specified circular aperture; determine the product between the first-class second-order Bessel function and the circular aperture function is a Bessel function.

In one embodiment, obtaining a composite vortex function based on the superposition of the positive vortex function and the negative vortex function includes: keeping the topological charge parameter as a preset integer, based on the filter polar angle and the set initial phase angle, respectively construct a positive vortex function and a negative vortex function; multiply one of the positive vortex function and the negative vortex function by a weighting factor, and perform a weighted summation to obtain a composite vortex function .

In an embodiment, modulating the spectrum information of the image to be processed based on the vortex filter to obtain the modulated spectrum information of the image to be processed includes: acquiring the image size of the image to be processed, and based on the image size to be processed. The image size adjusts the radius of the vortex filter and/or the edge enhancement amplitude value to obtain a frequency-domain vortex filter that modulates the spectral information of the image to be processed, wherein the frequency-domain vortex filter The filter makes the extracted edge image satisfy the evaluation value of the preset point spread function; modulates the spectral information of the to-be-processed image based on the frequency-domain vortex filter to obtain the modulated spectral information of the to-be-processed image.

According to yet another aspect of the embodiments of the present disclosure, there is provided an image edge extraction apparatus, comprising: an acquisition module for acquiring an image to be processed; a transformation module for performing Fourier transform on the to-be-processed image to obtain the to-be-processed image The spectrum information of the image is processed, and the inverse Fourier transform is performed on the modulated spectrum information to obtain the edge image of the image to be processed; the modulation module is used for performing the spectrum information of the image to be processed based on the vortex filter. modulation to obtain the modulated spectrum information of the image to be processed, wherein the transmittance function of the vortex filter is obtained by modulating a composite vortex function based on a Bessel function.

In one embodiment, the transmittance function of the vortex filter is obtained by modulating the composite vortex function based on the Bessel function in the following manner: based on the circular aperture function and the second-order Bessel function of the first type, determine the Bessel function. Searle function; based on the superposition of the positive vortex function and the negative vortex function, a composite vortex function is obtained; based on the Bessel function, the composite vortex function is modulated to obtain the transmittance function of the vortex filter .

In one embodiment, obtaining a composite vortex function based on the superposition of the positive vortex function and the negative vortex function includes: keeping the topological charge parameter as a preset integer, based on the filter polar angle and the set initial phase angle, respectively construct a positive vortex function and a negative vortex function; multiply one of the positive vortex function and the negative vortex function by a weighting factor, and perform a weighted summation to obtain a composite vortex function .

In one embodiment, obtaining a composite vortex function based on the superposition of the positive vortex function and the negative vortex function includes: keeping the topological charge parameter as a preset integer, based on the filter polar angle and the set initial phase angle, respectively construct a positive vortex function and a negative vortex function; multiply one of the positive vortex function and the negative vortex function by a weighting factor, and perform a weighted summation to obtain a composite vortex function .

In an embodiment, modulating the spectrum information of the image to be processed based on the vortex filter to obtain the modulated spectrum information of the image to be processed includes: acquiring the image size of the image to be processed, and based on the image size to be processed. The image size adjusts the radius of the vortex filter, and/or the amplitude value of the polar angle edge enhancement of the vortex filter, to obtain a frequency-domain vortex filter that modulates the spectral information of the image to be processed, wherein, The frequency domain vortex filter makes the extracted edge image satisfy the evaluation value of the preset point spread function; based on the frequency domain vortex filter, the spectral information of the to-be-processed image is modulated to obtain the to-be-processed image Modulated spectral information.

According to yet another aspect of the embodiments of the present disclosure, there is provided an image edge extraction apparatus, including: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to: execute any of the foregoing Image edge extraction method.

According to yet another aspect of the embodiments of the present disclosure, a non-transitory computer-readable storage medium is provided, when instructions in the storage medium are executed by a processor of a mobile terminal, the mobile terminal can execute the image described in any one of the preceding items edge extraction method.

The technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects: obtaining the spectral information of the to-be-processed image by performing Fourier transform on the to-be-processed image, and modulating the spectral information of the to-be-processed image based on the vortex filter to obtain the to-be-processed image The modulated spectral information, the transmittance function of the vortex filter is obtained by modulating the composite vortex function based on the Bessel function, and the inverse Fourier transform is performed on the modulated spectral information to obtain the edge image of the image to be processed, which can be treated. The processed image realizes high-contrast isotropic edge enhancement, and can enhance the anisotropic edge to improve the processing effect of edge extraction.

It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure.

Description of drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description serve to explain the principles of the disclosure.

Figure 1 shows a comparison diagram of the image processing effect of the traditional method and the Laguerre Gaussian modulation vortex filtering method.

Fig. 2 shows a comparison diagram of the image processing effect of the Laguerre Gaussian modulated vortex filtering method and the Airy function modulated vortex method.

Figures 3a and 3b show a comparison diagram of anisotropic edge enhancement effects based on Hilbert transform with different fraction values and different radial displacements in different directions.

FIG. 4 is a flowchart of a method for extracting an image edge according to an exemplary embodiment of the present disclosure.

FIG. 5 is a schematic diagram of an exemplary 4f imaging system according to the present disclosure.

FIG. 6 is a flowchart of a method for determining a transmittance function of a vortex filter according to an exemplary embodiment of the present disclosure.

FIG. 7 is a flowchart of a method for determining a Bessel vortex filter function in an image edge extraction method according to another exemplary embodiment of the present disclosure.

FIG. 8 is a flowchart of a method for determining a composite vortex function in an image edge extraction method according to another exemplary embodiment of the present disclosure.

FIG. 9 is a flowchart of an image edge extraction method according to another exemplary embodiment of the present disclosure.

FIG. 10 is a schematic diagram of an application scenario of an image edge extraction method according to another exemplary embodiment of the present disclosure.

FIG. 11 is an effect diagram of an image edge extraction method according to an exemplary embodiment of the present disclosure.

FIG. 12 is an effect diagram of an image edge extraction method according to an exemplary embodiment of the present disclosure.

FIG. 13 is an effect diagram of an image edge extraction method according to an exemplary embodiment of the present disclosure.

FIG. 14 is an effect diagram of an image edge extraction method according to an exemplary embodiment of the present disclosure.

FIG. 15 is an effect diagram of an image edge extraction method according to an exemplary embodiment of the present disclosure.

FIG. 16 is an effect diagram of an image edge extraction method according to an exemplary embodiment of the present disclosure.

FIG. 17 is an effect diagram of an image edge extraction method according to an exemplary embodiment of the present disclosure.

Fig. 18 is a block diagram of an image edge extraction apparatus according to an exemplary embodiment of the present disclosure.

Fig. 19 is a block diagram of an apparatus according to an exemplary embodiment.

Detailed ways

Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. Where the following description refers to the drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the illustrative examples below are not intended to represent all implementations consistent with this disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as recited in the appended claims.

With the in-depth development of optical information and image processing technology, edge is the most basic feature of an image, and edge detection is an important link in image analysis and recognition, and is the key to dealing with many problems. Other features of the image are derived from the basic features of edges and regions, and all the information in the image can be recovered through edge information. The effect of edge detection will directly affect the performance of image segmentation and recognition. Image edge detection technology plays an important role in computer vision, image analysis and other applications.

There are many types of edge detection algorithms, such as differential operator method, template matching method, wavelet detection method, neural network method and so on. Although there are many edge detection and edge extraction methods in the current technology, the images processed by these methods have problems such as reduced contrast, low edge sharpness, and high background noise.

In the current technology, the image edge detection method is based on the high-pass filtering method. The high-pass filtering method filters out low-frequency information in the image and causes most of the energy to be lost, and is mainly used for isotropic edge extraction or detection. The Hilbert transform enhances image contrast when applied to image edge detection because it is sensitive to complex refractive index gradients of object information. Radial Hilbert transform is often used in isotropic image edge enhancement techniques. Anisotropy is directional, such as the edge of an image, which is centered at a certain point, and the characteristics of each direction are different. On the contrary, it is non-directional and isotropic.

Among the isotropic edge extraction techniques based on Hilbert transform, there are Laguerre Gaussian modulated vortex filtering method and Airy function modulated vortex filtering method. Among them, the Laguerre Gaussian modulated vortex filter uses the Laguerre Gaussian function for amplitude modulation to reduce the diffraction of the incident light by the central singularity. In this method, the Laguerre amplitude is difficult to eliminate the straight-edge diffraction phenomenon of the incident light caused by the sharp edge of the filter, and the sidelobe distribution in the point spread function is obvious.

Figure 1 shows a comparison diagram of the image processing effect of the traditional method and the Laguerre Gaussian modulation vortex filtering method. Among them, Figure 1(a) shows the original image containing the Chinese character "mountain", Figure 1(b) shows the image processed by the traditional vortex filtering method, and Figure 1(c) shows the image using Laguerre The image processed by the Gaussian modulation vortex filter method,

In order to eliminate side lobes and improve image contrast, an Airy function modulated vortex filter is proposed. The Airy function modulated vortex filter can improve the sharpness and contrast of the image edge by changing the attenuation factor and eliminating the side lobes in the point spread function.

Fig. 2 shows a comparison diagram of the image processing effect of the Laguerre Gaussian modulated vortex filtering method and the Airy function modulated vortex method. Among them, Figure 2(a) shows the original image of the resolution class, Figure 2(b) shows the image processed by the Laguerre Gaussian modulation vortex filtering method, and Figure 2(c) shows the image using AI Figure 2(d), Figure 2(e) and Figure 2(f) are the images along the lines of Figure 2(a), Figure 2(b) and Figure 2(c), respectively. The intensity distribution curve of the dashed line.

In the anisotropic edge extraction technology based on Hilbert transform, the radial symmetry of the filtering process is broken, including fractional vortex filtering and off-axis vortex filtering. The fractional vortex filtering method breaks the radial symmetry of the filtering process, thereby generating controllable edge dislocations. By controlling the magnitude and initial phase of the fractional order, the degree and direction of edge enhancement are changed. As the fractional order decreases, Anisotropy is obvious in image edge extraction, but it will increase the shadow effect.

The off-axis vortex filter method obtains anisotropic edge enhancement effects in all directions by setting different angle and displacement parameters respectively. In the above-mentioned anisotropic edge extraction technology based on Hilbert transform, there is still obvious shadow effect on the image edge in the edge-extracted image, and a high-contrast edge detection effect cannot be obtained.

Figures 3a and 3b show a comparison diagram of anisotropic edge enhancement effects based on Hilbert transform with different fraction values and different radial displacements in different directions.

In practical applications, different edge features are emphasized according to the scene, and anisotropic edge enhancement technology needs to be used to emphasize these edges, and it is an urgent problem to achieve high-contrast isotropic and anisotropic edge detection.

Thus, the present disclosure provides an image edge extraction method for realizing high-contrast isotropic and anisotropic image edge extraction.

Fig. 4 is a flowchart of an image edge extraction method according to an exemplary embodiment of the present disclosure. As shown in Fig. 4 , the image edge extraction method includes the following steps.

In step S101, the image to be processed is acquired, and the spectrum information of the image to be processed is obtained by performing Fourier transform on the image to be processed.

In step S102, the spectral information of the image to be processed is modulated based on the vortex filter to obtain the modulated spectral information of the to-be-processed image, wherein the transmittance function of the vortex filter is based on the Bessel function to modulate the composite vortex function get.

In step S103, inverse Fourier transform is performed on the modulated spectrum information to obtain an edge image of the image to be processed.

In the embodiment of the present disclosure, the to-be-processed image is acquired, and the to-be-processed image may be collected by an image acquisition device or downloaded and received through a network as a saved image.

The image edge extraction method in the embodiment of the present disclosure may be implemented based on a 4f imaging system, and FIG. 5 is a schematic diagram of an exemplary 4f imaging system according to the present disclosure.

As shown in FIG. 5 , o(r ₀ , θ ₀ ) represents the object plane, O(ρ, φ) represents the Fourier plane, and o′(r ₀ , θ ₀ ) represents the image plane. The object plane and the Fourier plane are located at the front focal plane and the back focal plane of the lens L1, respectively, and the image plane is located at the back focal plane of the lens L2. The vortex filter is set in the Fourier plane, and the spectral information is modulated by the vortex filter on the Fourier plane. Using the system shown in Figure 5, the Hilbert transform is implemented by placing the vortex filter on the Fourier plane.

The object information of the image to be processed is subjected to Fourier transform through the lens L1, and the object spectrum information F[o(r ₀ , θ ₀ )] is obtained on the Fourier plane, wherein the symbol F[] represents the Fourier transform. After the Fourier plane is modulated by the designed filter, inverse Fourier transform is performed on the modulated spectral information to obtain the edge image of the image to be processed.

The modulated object spectrum information is expressed by multiplying the object spectrum by the transmittance function T(ρ, φ) of the filter, as shown in the following formula.

O(ρ, φ)=F[o(r ₀ , θ ₀ )]×T(ρ, φ)

The transmittance function T(ρ, φ) of the vortex filter is obtained by modulating the compound vortex function based on the Bessel function.

According to the embodiments of the present disclosure, the spectral information of the to-be-processed image is obtained by performing Fourier transform on the to-be-processed image, and the spectral information of the to-be-processed image is modulated based on the vortex filter to obtain the modulated spectral information of the to-be-processed image, and the vortex The transmittance function of the filter is obtained by modulating the composite vortex function based on the Bessel function, and the inverse Fourier transform is performed on the modulated spectral information to obtain the edge image of the image to be processed, which can achieve high contrast anisotropy of the image to be processed. Edge enhancement, to improve the processing effect of edge extraction.

FIG. 6 is a flowchart of a method for determining a transmittance function of a vortex filter according to an exemplary embodiment of the present disclosure. As shown in FIG. 6 , the method for determining the transmittance function includes the following steps.

In step S201, the Bessel function is determined based on the circular aperture function and the second-order Bessel function of the first type.

In step S202, a composite vortex function is obtained based on the superposition of the positive vortex function and the negative vortex function.

In step S203, the compound vortex function is modulated based on the Bessel function to obtain the transmittance function of the vortex filter.

In the embodiment of the present disclosure, the transmittance function of the vortex filter is obtained by modulating the composite vortex function based on the Bessel function, and the transmittance function T(ρ, φ) can be expressed by the following formula.

表示半径为R₀的圆形孔径，当∣ρ∣大于R₀时，圆形孔径函数值为0。即，贝塞尔函数为基于圆形孔径函数以及第一类二阶贝塞尔函数确定，实现了透过率函数T(ρ，φ)的渐变，从而降低噪声，提高对比度。where J ₂ is the second-order Bessel function of the first kind, circl() is the circular aperture function,

Represents a circular aperture with a radius of R _0. When ∣ρ∣ is greater than R ₀ , the circular aperture function value is 0. That is, the Bessel function is determined based on the circular aperture function and the first-type second-order Bessel function, which realizes the gradual change of the transmittance function T(ρ, φ), thereby reducing noise and improving contrast.

In the embodiment of the present disclosure, the composite vortex function is obtained based on the superposition of the positive vortex function and the negative vortex function. As shown in the above formula, the composite vortex function is exp[il(φ+φ ₀ )]+exp[-il(φ+φ ₀ )], where exp[il(φ+φ ₀ )] represents the positive vortex function , exp[-il(φ+φ ₀ )] represents the negative vortex function. The composite vortex function of positive vortex function and negative vortex function realizes isotropic edge extraction or anisotropic edge extraction of the image to be processed. The transmittance function of the vortex filter is obtained by modulating the composite vortex function based on the Bessel function.

According to the embodiments of the present disclosure, the compound vortex function is modulated by the Bessel function to obtain the transmittance function of the vortex filter, so that the image to be processed can be enhanced with high contrast and isotropic or anisotropic edges, so that the image The sharpness and contrast of edges are improved, improving the image processing effect of edge extraction.

FIG. 7 is a flowchart of a method for determining a Bessel vortex filter function in an image edge extraction method according to another exemplary embodiment of the present disclosure. As shown in FIG. 7 , the method for determining the Bessel vortex filter function includes the following steps.

In step S301, a first-type second-order Bessel function is determined based on the vortex filter radius and edge enhancement amplitude value modulation parameters, and a circular aperture function is determined based on the vortex filter radius and a designated circular aperture.

In step S302, the product between the second-order Bessel function of the first type and the circular aperture function is determined as a Bessel function.

中，ρ为涡旋滤波器半径，R₀为指定圆形孔径，基于涡旋滤波器半径ρ与指定圆形孔径R₀确定圆形孔径函数。α为边缘增强幅度值调制参数，第一类二阶贝塞尔函数J₂基于涡旋滤波器半径R₀与边缘增强幅度值调制参数确定。将第一类二阶贝塞尔函数J₂与圆形孔径函数ciccl()之间的乘积，确定为贝塞尔函数，实现了透过率函数T(ρ，φ)的渐变，从而降低噪声，提高对比度，实现了更高的对比度边缘检测、提取。In an embodiment of the present disclosure, the circular aperture function

where ρ is the radius of the vortex filter, R ₀ is the specified circular aperture, and the circular aperture function is determined based on the radius ρ of the vortex filter and the specified circular aperture R ₀ . α is the edge enhancement amplitude value modulation parameter, and the first type of second-order Bessel function J ₂ is determined based on the vortex filter radius R ₀ and the edge enhancement amplitude value modulation parameter. The product between the first-class second-order Bessel function J ₂ and the circular aperture function ciccl() is determined as a Bessel function, and the gradient of the transmittance function T(ρ, φ) is realized, thereby reducing noise , improve the contrast, and achieve higher contrast edge detection and extraction.

According to the embodiments of the present disclosure, the compound vortex function is modulated by the compound Bessel function to obtain the transmittance function of the vortex filter, so that the image to be processed can be enhanced with high contrast and isotropic or anisotropic edges, so that the The sharpness and contrast of the image edges are further improved.

FIG. 8 is a flowchart of a method for determining a composite vortex function in an image edge extraction method according to another exemplary embodiment of the present disclosure. As shown in FIG. 8 , the method for determining the composite vortex function includes the following steps.

In step S401, while keeping the topological charge parameter as a preset integer, a positive vortex function and a negative vortex function are respectively constructed based on the filter polar angle and the set initial phase angle.

In step S402, after multiplying one of the positive vortex function and the negative vortex function by a weighting factor, weighted summation is performed to obtain a composite vortex function.

In the embodiment of the present disclosure, the composite vortex function is obtained based on the superposition of the positive vortex function and the negative vortex function, and the positive vortex function and the negative vortex function are respectively constructed based on the filter polar angle and the set initial phase angle. . The compound vortex function, namely cexp[il(φ+φ ₀ )]+exp[-il(φ+φ ₀ )], where l represents the topological charge number parameter, and l is an integer. Exemplarily, the topological charge number parameter l may take a value of 1, and the filter polar angle φ, 0≤φ<2π. The set initial phase angle φ ₀ is used to control the direction of edge enhancement. The initial phase _φ0 is set to different angle values to control the edge enhancement in different directions. For example, the initial phase angle φ ₀ may be 0, π/4, π/2, and 3π/4. The weighting factor c is used to represent the weight ratio of the positive vortex and the negative vortex, and the magnitude of edge enhancement varies with the value of the weighting factor c. For example, when c=0, isotropic edge enhancement is obtained by this filter.

According to an embodiment of the present disclosure, by constructing a positive vortex function and a negative vortex function respectively based on the filter polar angle and the set initial phase angle, one of the positive vortex function and the negative vortex function is multiplied by After the weighting factor, the weighted summation is performed to obtain the composite vortex function, which realizes isotropic or anisotropic edge enhancement in different directions and different enhancement amplitudes, and the edge image extraction effect of the image to be processed is better.

FIG. 9 is a flowchart of an image edge extraction method according to another exemplary embodiment of the present disclosure. As shown in Figure 9, the image edge extraction method includes the following steps.

In step S501, the image size of the image to be processed is obtained, and the radius of the vortex filter and/or the edge enhancement amplitude value are adjusted based on the image size to obtain a frequency domain vortex filter that modulates the spectral information of the image to be processed, wherein , the frequency domain vortex filter makes the extracted edge image satisfy the evaluation value of the preset point spread function.

In step S502, the spectrum information of the image to be processed is modulated based on the frequency domain vortex filter to obtain modulated spectrum information of the image to be processed.

中，基于获取到的待处理图像的图像尺寸，调整α和/或ρ，得到对待处理图像的频谱信息进行调制的频域涡旋滤波器，以实现透过率函数T(ρ，φ)的渐变，降低噪声，提高对比度。In the embodiment of the present disclosure, the image size of the image to be processed is acquired, including the size of the image to be processed, that is, the number of pixels, and the pixel size of the image to be processed. Adjust the vortex filter radius, and/or the edge enhancement magnitude value based on the image size, i.e. the formula

, based on the obtained image size of the image to be processed, adjust α and/or ρ to obtain a frequency-domain vortex filter that modulates the spectral information of the image to be processed, so as to realize the change of the transmittance function T(ρ, φ). Gradient, reduce noise, increase contrast.

By Fourier transform, the point spread function of the 4f system in polar coordinates can be expressed as:

Among them, (r ₁ , θ ₁ ) represents the polar coordinates on the image plane, f2 is the focal length of the lens L2 , and J ₁ is the first-order first-order Bessel function. The extracted edge image satisfies the evaluation value of the preset point spread function, that is, the spectral information of the image to be processed is modulated based on the frequency domain vortex filter to obtain the spectral information of the image to be processed, and the energy is more concentrated in the main lobe located in the center , the sidelobe noise is better suppressed.

According to an embodiment of the present disclosure, by adjusting the radius of the vortex filter and/or the edge enhancement amplitude value based on the acquired image size of the image to be processed, a frequency domain vortex filter that modulates the spectral information of the image to be processed is obtained, Based on the frequency-domain vortex filter, the spectral information of the image to be processed is modulated to obtain the modulated spectral information of the to-be-processed image. The frequency-domain vortex filter makes the extracted edge image satisfy the evaluation value of the preset point spread function. Adaptive vortex filter, edge extraction of images to be processed with different image sizes.

FIG. 10 is a schematic diagram of an application scenario of an image edge extraction method according to another exemplary embodiment of the present disclosure.

For example, a 532nm single-mode solid-state laser with a power of 300mw is used as the light source, which is expanded and collimated, and the information of the carried object is amplified through the objective lens and the lens L1. A diaphragm is arranged between the lens L1 and the lens L2 to block the remaining diffracted light. Lenses L2 and L3 form a 4f imaging system, which performs Fourier transform and inverse Fourier transform respectively. The spatial light modulator is placed on the back focal plane of lens L2, loads a hologram, and modulates the spectral information of the object. The complex amplitude of the vortex filter is encoded into a hologram by means of a blazed grating, and the modulated complex amplitude can be obtained in the positive first-order diffraction of the fork grating. Under the reflection of the spatial light modulator and the beam splitting prism, the first-order diffracted beam is Fourier transformed through the lens L3, and the output image is recorded on the camera at the focal plane, and the loaded hologram and the recorded image result are controlled by the computer .

In an embodiment of the present disclosure, fractional vortex filtering, off-axis vortex filtering, conventional superimposed vortex filtering, Laguerre Gaussian modulation superimposed vortex filtering, and the Bessel function-based compound vortex function of the present disclosure are used to obtain The point spread function of the vortex filter was simulated.

FIG. 11 is an effect diagram of an image edge extraction method according to an exemplary embodiment of the present disclosure. In Figure 11, a1-e1 are the point spread functions of fractional vortex filtering, off-axis vortex filtering, conventional superimposed vortex filtering, Laguerre-Gaussian modulation superimposed vortex filtering, and Bessel modulation superimposed vortex filtering. In Fig. 11, a2-e2 are the radial cross-section intensity distribution of the point spread function drawn in a1-e1 respectively, and the maximum value is normalized to obtain the amplitude curve of the radial cross-section.

Referring to FIG. 11 , the point spread function intensity distributions of the fractional vortex filter and the off-axis vortex filter are asymmetric, and there are also strong redundant side lobes in the above filters. The point spread function of the conventional stacked vortex filter maintains radial symmetry, but the unwanted sidelobes are still evident. Laguerre Gaussian modulation superimposed with vortex filtering, the side lobes of the point spread function are partially suppressed. The filter of the present disclosure can effectively suppress the side lobes, maintain the radial symmetry of the filtering process, the energy is more concentrated on the main lobe, and the side lobe noise is better suppressed.

In an embodiment of the present disclosure, fractional vortex filtering, off-axis vortex filtering, conventional superimposed vortex filtering, Laguerre-Gaussian modulation superimposed vortex filtering, and the Bessel function-based superimposed vortex function of the present disclosure are applied to edge The effects of detection or extraction are compared.

FIG. 12 is an effect diagram of an image edge extraction method according to an exemplary embodiment of the present disclosure, and a1-e1 in FIG. 12 are fractional vortex filtering, off-axis vortex filtering, conventional superimposed vortex filtering, and cover pulling, respectively. Simulation results of Gaussian modulation superimposed vortex filtering and Bessel modulation superimposed vortex filtering. a2-e2 in FIG. 12 are the strength cross-section steps along the dotted lines in a1-e1 in FIG. 12, respectively.

Referring to FIG. 12 , applying fractional vortex filter and off-axis vortex filter, the shadowing effect becomes severe because the symmetry of the filtering process is broken. There are side lobes in the processing results of fractional vortex filter, off-axis vortex filter and conventional superposition vortex filter, and the background noise is significant. Laguerre-Gaussian-modulated superimposed vortex filtering can change the amplitude of singularities, blocking diffracted light from singularities, for higher contrast edge detection or extraction than conventional superimposed vortex filters. The contrast of the conventional superimposed vortex filter is only 0.62, and the contrast of the image edge extraction method of the embodiment of the present disclosure is 0.98, which has good performance in reducing background noise, and improves imaging resolution and contrast.

In an embodiment of the present invention, when the initial phase angle φ ₀ is set to different angle values, edge enhancement in different directions is controlled. When the weighting factor c is equal to 1, edge enhancement in different directions can be obtained by setting the initial phase angle φ0 to ₀ , π/4, π/2 and 3π/4. By changing the value of the weighting factor c to adapt to the situation in the actual application process, the detection or extraction of anisotropic edges can be realized with changing power. For example, φ ₀ sets π/2, and changes the value of the weighting factor c from 0 to 1 to achieve a change in the magnitude of edge enhancement in the horizontal direction.

Fig. 13 is an effect diagram of an image edge extraction method according to an exemplary embodiment of the present disclosure. In Fig. 13 (a)-(d), the initial phase angle φ0 is set to ₀ , π/4, π/ Simulation results for 2 and 3π/4.

FIG. 14 is an effect diagram of an image edge extraction method according to an exemplary embodiment of the present disclosure, and (a)-(e) in FIG. 14 show that φ ₀ is set to π/2 and the weighting factor c of different values simulation results. In (a)-(e), the value of the weighting factor c is taken as 0, 0.3, 0.5, 0.7 and 1, respectively. Figure 14(f) is the intensity distribution along the dotted line of Figures 10(a)-(e).

FIG. 15 is an effect diagram of an image edge extraction method according to an exemplary embodiment of the present disclosure, and (a) and (b) of FIG. 15 are respectively using conventional superimposed vortex filtering and applying the vortex of the embodiment of the present disclosure Experimental results of the filter function. (c) and (d) are the strength cross-sectional distributions of (a) and (b) along the dashed lines, respectively. The objects in Figure 15 are the letters "AF".

Fig. 16 is an effect diagram of an image edge extraction method according to an exemplary embodiment of the present disclosure, in Fig. 16 (a)-(d) are the anisotropic edge enhancement effects of the letter "1X", (e)- (h) is the anisotropic edge enhancement effect of the four ancient coins.

FIG. 17 is an effect diagram of an image edge extraction method according to an exemplary embodiment of the present disclosure. (a)-(e) in FIG. 17 show the vortex filter function applying the embodiment of the present disclosure, and φ ₀ is set The experimental results for π/2 and different values of the weighting factor c, Fig. 17(f) is the intensity distribution of (a)-(e) along the dotted line, the object is the letter "AF".

Based on the same concept, an embodiment of the present disclosure also provides an image edge extraction apparatus.

It can be understood that, in order to realize the above-mentioned functions, the image edge extraction apparatus provided by the embodiments of the present disclosure includes corresponding hardware structures and/or software modules for executing each function. Combining with the units and algorithm steps of each example disclosed in the embodiments of the present disclosure, the embodiments of the present disclosure can be implemented in hardware or a combination of hardware and computer software. Whether a function is performed by hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the technical solutions of the embodiments of the present disclosure.

Fig. 18 is a block diagram of an image edge extraction apparatus according to an exemplary embodiment. Referring to FIG. 18 , the image edge extraction apparatus 100 includes an acquisition module 101 , a transformation module 102 and a modulation module 103 .

The acquiring module 101 is used for acquiring the image to be processed.

The transformation module 102 is configured to perform Fourier transform on the image to be processed to obtain spectral information of the image to be processed, and perform inverse Fourier transform on the modulated spectral information to obtain an edge image of the image to be processed.

The modulation module 103 is configured to modulate the spectral information of the image to be processed based on the vortex filter to obtain the modulated spectral information of the image to be processed, wherein the transmittance function of the vortex filter modulates the composite vortex based on the Bessel function function get.

In one embodiment, the transmittance function of the vortex filter is obtained by modulating the composite vortex function based on the Bessel function in the following manner: based on the circular aperture function and the first-class second-order Bessel function, the Bessel function is determined. function; based on the superposition of the positive vortex function and the negative vortex function, the composite vortex function is obtained; the transmittance function of the vortex filter is obtained by modulating the composite vortex function based on the Bessel function.

In one embodiment, obtaining a composite vortex function based on the superposition of the positive vortex function and the negative vortex function includes: keeping the topological charge parameter as a preset integer, based on the filter polar angle and the set initial phase angle, respectively construct the positive vortex function and the negative vortex function; multiply one of the positive vortex function and the negative vortex function by the weighting factor, and perform weighted summation to obtain the composite vortex function.

In one embodiment, obtaining a composite vortex function based on the superposition of the positive vortex function and the negative vortex function includes: keeping the topological charge parameter as a preset integer, based on the filter polar angle and the set initial phase angle, respectively construct the positive vortex function and the negative vortex function; multiply one of the positive vortex function and the negative vortex function by the weighting factor, and perform weighted summation to obtain the composite vortex function.

In one embodiment, modulating the spectral information of the image to be processed based on the vortex filter to obtain the modulated spectral information of the image to be processed includes: acquiring the image size of the image to be processed, and adjusting the radius of the vortex filter based on the image size , and/or the polar angle edge enhancement amplitude value of the vortex filter to obtain a frequency-domain vortex filter that modulates the spectral information of the image to be processed, wherein the frequency-domain vortex filter makes the extracted edge image satisfy the preset point diffusion The evaluation value of the function; based on the frequency domain vortex filter, the spectral information of the image to be processed is modulated to obtain the modulated spectral information of the image to be processed.

Regarding the apparatus in the above-mentioned embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be described in detail here.

FIG. 19 is a block diagram of an image edge extraction apparatus 800 according to an exemplary embodiment. For example, apparatus 800 may be a mobile phone, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, fitness device, personal digital assistant, and the like.

19, the apparatus 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, and communication component 816.

The processing component 802 generally controls the overall operation of the device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 can include one or more processors 820 to execute instructions to perform all or some of the steps of the methods described above. Additionally, processing component 802 may include one or more modules that facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

Memory 804 is configured to store various types of data to support operations at device 800 . Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, and the like. Memory 804 may be implemented by any type of volatile or nonvolatile storage device or combination thereof, such as static random access memory (SRAM), electrically erasable programmable read only memory (EEPROM), erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic or Optical Disk.

Power component 806 provides power to various components of device 800 . Power components 806 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to device 800 .

Multimedia component 808 includes a screen that provides an output interface between the device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touch, swipe, and gestures on the touch panel. The touch sensor may not only sense the boundaries of a touch or swipe action, but also detect the duration and pressure associated with the touch or swipe action. In some embodiments, the multimedia component 808 includes a front-facing camera and/or a rear-facing camera. When the apparatus 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera may receive external multimedia data. Each of the front and rear cameras can be a fixed optical lens system or have focal length and optical zoom capability.

Audio component 810 is configured to output and/or input audio signals. For example, audio component 810 includes a microphone (MIC) that is configured to receive external audio signals when device 800 is in operating modes, such as call mode, recording mode, and voice recognition mode. The received audio signal may be further stored in memory 804 or transmitted via communication component 816 . In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module, which may be a keyboard, a click wheel, a button, or the like. These buttons may include, but are not limited to: home button, volume buttons, start button, and lock button.

Sensor assembly 814 includes one or more sensors for providing status assessment of various aspects of device 800 . For example, the sensor assembly 814 can detect the open/closed state of the device 800, the relative positioning of components, such as the display and keypad of the device 800, and the sensor assembly 814 can also detect a change in the position of the device 800 or a component of the device 800 , the presence or absence of user contact with the device 800 , the orientation or acceleration/deceleration of the device 800 and the temperature change of the device 800 . Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

Communication component 816 is configured to facilitate wired or wireless communication between apparatus 800 and other devices. Device 800 may access wireless networks based on communication standards, such as WiFi, 2G or 3G, or a combination thereof. In one exemplary embodiment, the communication component 816 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

In an exemplary embodiment, apparatus 800 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable A gate array (FPGA), controller, microcontroller, microprocessor or other electronic component implementation is used to perform the above method.

In an exemplary embodiment, there is also provided a non-transitory computer-readable storage medium including instructions, such as a memory 804 including instructions, executable by the processor 820 of the apparatus 800 to perform the method described above. For example, the non-transitory computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like.

It should be understood that in the present disclosure, "plurality" refers to two or more than two, and other quantifiers are similar. "And/or", which describes the association relationship of the associated objects, means that there can be three kinds of relationships, for example, A and/or B, which can mean that A exists alone, A and B exist at the same time, and B exists alone. The character "/" generally indicates that the associated objects are an "or" relationship. The singular forms "a," "the," and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise.

It is further understood that the terms "first", "second", etc. are used to describe various information, but the information should not be limited to these terms. These terms are only used to distinguish the same type of information from one another, and do not imply a particular order or level of importance. In fact, the expressions "first", "second" etc. are used completely interchangeably. For example, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information, without departing from the scope of the present disclosure.

It should be further understood that, unless otherwise specified, "connection" includes a direct connection between the two without other components, and also includes an indirect connection between the two with other elements.

It is further to be understood that, although the operations in the embodiments of the present disclosure are described in a specific order in the drawings, it should not be construed as requiring that the operations be performed in the specific order shown or the serial order, or requiring Perform all operations shown to obtain the desired result. In certain circumstances, multitasking and parallel processing may be advantageous.

Other embodiments of the present disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or techniques in the technical field not disclosed by the present disclosure . The specification and examples are to be regarded as exemplary only, with the true scope and spirit of the disclosure being indicated by the following claims.

It is to be understood that the present disclosure is not limited to the precise structures described above and illustrated in the accompanying drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims (12)

1. An image edge extraction method, comprising:

acquiring an image to be processed, and performing Fourier transform on the image to be processed to obtain frequency spectrum information of the image to be processed;

modulating the frequency spectrum information of the image to be processed based on a vortex filter to obtain the modulated frequency spectrum information of the image to be processed, wherein the transmittance function of the vortex filter is obtained by modulating a composite vortex function based on a Bessel function;

and carrying out inverse Fourier transform on the modulated frequency spectrum information to obtain an edge image of the image to be processed.

2. The image edge extraction method according to claim 1, wherein the transmittance function of the vortex filter is obtained by modulating the composite vortex function based on the Bessel function in the following manner:

determining a Bezier function based on the circular aperture function and the first second-order Bezier function;

obtaining a composite vortex function based on superposition of the positive vortex function and the negative vortex function;

and modulating the composite vortex function based on the Bessel function to obtain a transmittance function of the vortex filter.

3. The image edge extraction method according to claim 2, wherein determining the bezier function based on the circular aperture function and the first class of second-order bezier functions comprises:

determining a first-class second-order Bessel function based on the radius of the vortex filter and the modulation parameter of the edge enhancement amplitude value, and determining a circular aperture function based on the radius of the vortex filter and the specified circular aperture;

determining a product between the first class second order Bezier function and the circular aperture function as a Bezier function.

4. The image edge extraction method according to claim 2 or 3, wherein obtaining a composite vortex function based on superposition of the positive vortex function and the negative vortex function comprises:

respectively constructing a positive vortex function and a negative vortex function based on a polar angle of a filter and a set initial phase angle under the condition of keeping a topological charge parameter as a preset integer;

and multiplying one of the positive vortex function and the negative vortex function by a weighting factor, and then carrying out weighted summation to obtain a composite vortex function.

5. The image edge extraction method according to claim 4, wherein modulating the spectral information of the image to be processed based on a vortex filter to obtain the modulated spectral information of the image to be processed comprises:

acquiring the image size of the image to be processed, and adjusting the radius of the vortex filter and/or the edge enhancement amplitude value based on the image size to obtain a frequency domain vortex filter for modulating the frequency spectrum information of the image to be processed, wherein the frequency domain vortex filter enables the extracted edge image to meet the evaluation value of a preset point spread function;

and modulating the frequency spectrum information of the image to be processed based on the frequency domain vortex filter to obtain the modulated frequency spectrum information of the image to be processed.

6. An image edge extraction device, comprising:

the acquisition module is used for acquiring an image to be processed;

the transformation module is used for carrying out Fourier transformation on the image to be processed to obtain frequency spectrum information of the image to be processed and carrying out inverse Fourier transformation on the modulated frequency spectrum information to obtain an edge image of the image to be processed;

and the modulation module is used for modulating the frequency spectrum information of the image to be processed based on a vortex filter to obtain the modulated frequency spectrum information of the image to be processed, wherein the transmittance function of the vortex filter is obtained by modulating a composite vortex function based on a Bessel function.

7. The image edge extraction device according to claim 6, wherein the transmittance function of the vortex filter is obtained by modulating the composite vortex function based on the Bessel function in the following manner:

determining a Bezier function based on the circular aperture function and the first second-order Bezier function;

obtaining a composite vortex function based on superposition of the positive vortex function and the negative vortex function;

and modulating the composite vortex function based on the Bessel function to obtain a transmittance function of the vortex filter.

8. The image edge extraction device according to claim 7, wherein obtaining a composite vortex function based on a superposition of the positive vortex function and the negative vortex function comprises:

respectively constructing a positive vortex function and a negative vortex function based on a polar angle of a filter and a set initial phase angle under the condition of keeping a topological charge parameter as a preset integer;

and multiplying one of the positive vortex function and the negative vortex function by a weighting factor, and then carrying out weighted summation to obtain a composite vortex function.

9. The image edge extraction device according to claim 7 or 8, wherein obtaining a composite vortex function based on superposition of the positive vortex function and the negative vortex function comprises:

respectively constructing a positive vortex function and a negative vortex function based on a polar angle of a filter and a set initial phase angle under the condition of keeping a topological charge parameter as a preset integer;

and multiplying one of the positive vortex function and the negative vortex function by a weighting factor, and then carrying out weighted summation to obtain a composite vortex function.

10. The image edge extraction device according to claim 9, wherein modulating the spectral information of the image to be processed based on a vortex filter to obtain the modulated spectral information of the image to be processed comprises:

acquiring the image size of the image to be processed, and adjusting the radius of the vortex filter and/or the polar angle edge enhancement amplitude value of the vortex filter based on the image size to obtain a frequency domain vortex filter for modulating the frequency spectrum information of the image to be processed, wherein the frequency domain vortex filter enables the extracted edge image to meet the evaluation value of a preset point spread function;

and modulating the frequency spectrum information of the image to be processed based on the frequency domain vortex filter to obtain the modulated frequency spectrum information of the image to be processed.

11. An image edge extraction device, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to: performing the image edge extraction method of any one of claims 1 to 5.

12. A non-transitory computer readable storage medium, instructions in which, when executed by a processor of a mobile terminal, enable the mobile terminal to perform the image edge extraction method of any one of claims 1 to 5.

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