CN115773997A - Portable transparent turbid medium imaging device and imaging method - Google Patents

Abstract

The invention discloses a portable imaging device and imaging method through a turbid medium, belonging to the technical field of scattering medium imaging. The device includes an intelligent terminal with a shooting function, a card slot and a laser. By collecting multiple fuzzy images after passing through the turbid medium under laser irradiation, the corresponding point spread function is separated from each fuzzy image, and then the information in the multiple data is integrated and reconstructed by iterative deconvolution method to suppress the high frequency At the same time, the information of multiple pictures can be fused under the turbid medium, which improves the reconstruction quality of the algorithm, and the device is easy to operate, which can effectively reduce the cost of imaging, and can be used in daily life, increasing the practicality of the device sex.

Description

A portable imaging device and imaging method through turbid media

technical field

The invention relates to a portable imaging device and an imaging method through a turbid medium, belonging to the technical field of scattering medium imaging.

Background technique

In daily life, it is usually possible to observe the macroscopic and microscopic structures of objects through visible light, and sometimes it is necessary to observe objects through turbid media such as rivers, milk solutions, and skin tissues between the observed object and the observer. In the disorder of spatial distribution, the propagation direction of light in which the light propagates changes, that is, the light is scattered, and visible light can only travel a very limited distance in the turbid medium, so that the light incident into the turbid medium is lost when it exits. The spatial relative position of the original incident light field will lead to the degradation of the image quality of the observed object, so that the intuitive information of the observed object cannot be obtained. Therefore, how to achieve clear imaging of the observed object from the scene of river, milk solution, skin tissue and other scenes that are actually imaged through the turbid medium has important research significance in the fields of biology and medicine.

At present, there are two main methods for obtaining high-quality target images through turbid media:

(1) Improve imaging equipment or choose different imaging equipment according to different occasions. The mainstream imaging equipment has defects such as expensive, bulky equipment, and cumbersome operation. Other small imaging equipment, such as CCD, CMOS and other imaging components, also have the problems of high price, need to be equipped with a computer, and inconvenient data processing. In daily life, mobile imaging devices can be widely used in foggy weather photography, underwater photography and other application scenarios, but due to the cost and volume constraints of imaging equipment and image processing equipment, they have not been practically promoted.

(2) Improve the target detection algorithm, and perform targeted image processing on the collected images in the later stage. Scientific researchers have continuously improved optical imaging technology, and invented methods such as wavefront modulation, image reconstruction, and speckle correlation to process images transmitted through turbid media. However, based on the scattering characteristics of light passing through turbid media, detectors can only receive limited information about scattered objects, resulting in relatively low resolutions of objects reconstructed by these methods. Moreover, the use of conventional optical imaging technology has many problems such as high computational complexity, long calculation time, and large amount of data required, which often cannot achieve the purpose of observation. Especially not suitable for daily shooting and other scenarios, such as daily foggy photography, underwater photography and other application scenarios, the equipment used by the photographer may only be mobile phones and other portable devices, which do not have good computing power.

Contents of the invention

In order to solve the problem of blurred photos taken in daily foggy photography, underwater photography and other application scenarios, the present invention provides a portable imaging device through turbid media, the device includes a smart terminal, a card slot and a laser; wherein, the The smart terminal has a camera function, and the card slot is used to fix the laser on the smart terminal, so that the smart terminal can collect images irradiated by the laser.

Optionally, the smart terminal includes: a smart phone, a handheld computer and a smart watch.

Optionally, the working wavelength range of the laser is 390nm-780nm, the beam diameter is <10mm, the beam divergence angle is <0.5mrad, and the line width is <1.0cm ^-1 .

The present application also provides a portable method for imaging through turbid media, the method is realized based on the above-mentioned device, and the method includes:

Step S1: Collect n fuzzy images after passing through the turbid medium under laser irradiation, marked as I(x,y;n), n=1,2,3...;

Step S2: Separate the corresponding intensity point spread functions from the collected n blurred images and update them to obtain the updated intensity point spread function I _pr (x, y; n);

Step S3: Perform Fourier transform on the updated intensity point spread function I _pr (x, y; n) and the blurred image I (x, y; n) respectively to obtain their respective spectra, which are denoted as H(u, v; n) and I(u, v; n);

Step S4: Set a random object spectrum guess value as the initial value O _g (u, v, 1) of the object guess spectrum information;

Step S5: According to the spectral information of the object and the frequency spectrum of the updated intensity point spread function I _pr (x, y; n) obtained in step S3, it represents the spectrum I _g (u, v; n) of the guessed blurred image ;

Step S6: Calculate the difference I _d (u) between the spectrum I _g (u, v; n) of the guessed blurred image obtained in step S5 and the spectrum I (u, v; n) of the blurred image obtained in step S3 , v; n);

Step S7: Update the guessed spectrum information of the object according to the difference calculated in step S6, obtain the guessed spectrum information of the object after iterative update, repeat steps S5 to S7 until the iterative update of the collected n fuzzy images is completed to obtain the final iteratively updated Spectrum information O _g ′(u,v;n) guessed by the object, or the guessed spectrum information O _g ′(u,v;k) of the object after the final iterative update is obtained after satisfying the preset conditions, k≤n;

Step S8: Perform inverse Fourier transform on the estimated spectral value O _g ′(u,v;n) or O _g ′(u,v;k) of the object after the final iterative update to obtain the reconstructed object image o(x,y) .

Optionally, the step S2 includes:

2.1 The bright spot area irradiated by the laser beam on the surface of the object is used as the intensity point spread function image, which is marked as I _p (x, y; n);

The specific size of the bright spot area is determined according to the beam diameter, beam divergence angle and line width of the laser;

2.2: Set a zero matrix I _pr , the matrix size is the same as I(x, y; n), use the intensity point spread function to use the intensity point spread function for the area at the center of the zero matrix I _pr whose size is the same as I (x, y; n) The image I _p (x, y; n) is replaced to obtain an updated intensity point spread function image I _pr (x, y; n).

Optionally, the step S5 includes:

The spectrum I _g (u, v; n) of the guessed blurred image is obtained according to the following formula:

I _g (u, v; n) = O _g (u, v; n) H (u, v; n)

Wherein, H(u, v; n)=F{I _pr (x, y; n)}, is the frequency spectrum after the Fourier transform of the updated intensity point spread function image I _pr (x, y; n), F{} is the Fourier transform operator.

Optionally, in the step S7, the guessed spectrum information of the object is updated according to the difference calculated in the step S6, and the guessed spectrum information of the object after iterative update is obtained, including:

Update the guessed spectral information of the object according to the following formula:

where H ^* (u,v;n) represents the complex conjugate function of the spectrum H(u,v;n) of the intensity point spread function, and ξ represents the regularization parameter.

Optionally, in step S7, repeating steps S5 to S7 until the iterative update of the collected n fuzzy images is completed to obtain the guessed spectral information O _g '(u, v; n) of the object after the final iterative update, the previous The guessed spectrum information of the object obtained by the iterative update of a blurred image is used as the basis for calculating the spectrum of the guessed blurred image when the next blurred image is iteratively updated.

Optionally, the preset condition in step S7 means that the reconstructed image meets a predetermined accuracy, and the judging method includes:

Calculate the mean E(u,v;n) and variance σ(u,v;n) of the guessed spectral information O _g ′(u,v;n) of the object after each update iteration, and introduce the focusing standard TC based on the tamura coefficient :

The critical condition n ₀ is set, and when the calculated TC is greater than n ₀ , the guessed spectrum information O _g ′(u,v;k) of the object at this time is output, where k≤n.

Optionally, the turbid medium includes mist, water, milk solution, blood and skin tissue.

The beneficial effects of the present invention are:

(1) The present invention discloses a portable imaging device through turbid media. The imaging device integrates a smart phone, a card slot, and a small laser, and uses the camera that comes with the smart phone to quickly collect experimental data. The imaging device is easy to operate, can effectively reduce the cost of imaging, is not only applicable to the field of scientific research, but also can be used in daily life, increasing the practicability of the device.

(2) The portable imaging method through the turbid medium provided by the present invention integrates the developed imaging reconstruction algorithm on the mobile phone APP. The imaging reconstruction algorithm is based on the Wiener filter method to suppress noise, and uses iterative rollback The product method integrates and reconstructs the information in multiple data, suppresses high-frequency noise, and realizes the fusion of information in multiple pictures under turbid media, improving the reconstruction quality of the algorithm. The imaging platform is easy to use, has high imaging reconstruction efficiency, and can reconstruct images with high quality, thereby improving the imaging effect through turbid media.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings that need to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those skilled in the art, other drawings can also be obtained based on these drawings without creative effort.

Figure 1 is a schematic diagram of a portable imaging device through a turbid medium of the present invention;

Fig. 2 is a front view of the portable imaging device through turbid media of the present invention; wherein, 1-smart phone, 2-card slot, 3-small laser.

FIG. 3 is a flow chart of the actual use of the portable imaging device through turbid media of the present invention.

Fig. 4 is the fuzzy image that simulation experiment process gathers in one embodiment of the present application;

Fig. 5 is a comparison diagram of the reference pattern and the reconstructed image in the simulation experiment process in one embodiment of the present application, wherein (a) is the reference pattern, and (b) is the reconstructed image.

Detailed ways

In order to make the object, technical solution and advantages of the present invention clearer, the implementation manner of the present invention will be further described in detail below in conjunction with the accompanying drawings.

Embodiment one:

This embodiment provides a portable imaging device through turbid media, as shown in FIG. 1 , the device includes an intelligent terminal 1 , a card slot 2 and a small laser 3 . The smart terminal 1 has a camera function, and can be a smart phone, a handheld computer or other portable smart devices with a camera function. This embodiment will be described later by taking a currently commonly used portable device, a smart phone, as an example. The card slot 2 is used for The small laser 3 is fixed on the mobile phone, so that the smart phone 1 can take images under laser irradiation.

The specific requirements for small lasers 3 are:

Working wavelength: 390nm-780nm;

Maximum repetition rate: 50Hz;

Single pulse energy: 100-500Mj;

Pulse width: 3ns-8ns;

Short-term energy stability: ±3%;

Long-term power stability drift qualitative: <5%;

Spot mode: single transverse mode;

Gaussian coincidence rate: >70% (near field), >90% (far field);

Modulation degree: <40%;

Beam diameter: <10mm;

Beam pointing stability: <±50μrad;

Beam divergence angle: <0.5mrad;

Line width: <1.0cm ^-1 ;

Timing Jitter: <0.5ns.

The smart phone 1 reconstructs a clear image from multiple blurred images under laser irradiation according to the following method.

The methods include:

Step S1: Collect n fuzzy images after passing through the turbid medium under laser irradiation, marked as I(x,y;n), n=1,2,3...;

Step S2: Separate the corresponding intensity point spread functions from the collected n blurred images and update them to obtain the updated intensity point spread function I _pr (x, y; n);

Step S3: Perform Fourier transform on the updated intensity point spread function I _pr (x, y; n) and the blurred image I (x, y; n) respectively to obtain their respective spectra, which are denoted as H(u, v; n) and I(u, v; n);

Step S4: Set a random object spectrum guess value as the initial value O _g (u, v, 1) of the object guess spectrum information;

Step S5: According to the spectral information of the object and the frequency spectrum of the updated intensity point spread function I _pr (x, y; n) obtained in step S3, it represents the spectrum I _g (u, v; n) of the guessed blurred image ;

Step S6: Calculate the difference I _d (u) between the spectrum I _g (u, v; n) of the guessed blurred image obtained in step S5 and the spectrum I (u, v; n) of the blurred image obtained in step S3 , v; n);

Step S7: Update the guessed spectrum information of the object according to the difference calculated in step S6, obtain the guessed spectrum information of the object after iterative update, repeat steps S5 to S7 until the iterative update of the collected n fuzzy images is completed to obtain the final iteratively updated Spectrum information O _g ′(u,v;n) guessed by the object, or the guessed spectrum information O _g ′(u,v;k) of the object after the final iterative update is obtained after satisfying the preset conditions, k≤n;

Step S8: Perform inverse Fourier transform on the estimated spectral value O _g ′(u,v;n) or O _g ′(u,v;k) of the object after the final iterative update to obtain the reconstructed object image o(x,y) .

Embodiment two:

This embodiment provides a portable imaging method through turbid media, the method comprising:

Step 1: Turn on the small laser 3, and use the smart phone 1 to collect multiple fuzzy images after passing through the turbid medium, marked as I(x,y;n), where n=1,2,3..., indicating the sequence number of the captured images ; Turbid media include fog, water, milk solution, skin tissue, etc.

In the follow-up experiment of this embodiment, 5 fuzzy images were collected, as shown in FIG. 4 , which were obtained by shooting through the milk solution.

Step 2: Separate the corresponding intensity point spread functions from the multiple blurred images collected and update them, specifically, including:

2.1 The bright spot area irradiated by the laser beam on the surface of the object is used as the intensity point spread function image, which is marked as I _p (x, y; n);

The specific size of the bright spot area can be determined according to the beam diameter, beam divergence angle and line width of the laser.

2.2: Set a zero matrix I _pr , the matrix size is the same as I(x, y; n), use the intensity point spread function to use the intensity point spread function for the area at the center of the zero matrix I _pr whose size is the same as I (x, y; n) The image I _p (x, y; n) is replaced, and the updated intensity point spread function image I _pr (x, y; n) is obtained;

The updated intensity point spread function image I _pr (x, y; n) is used to represent the transmission characteristics of the entire image.

Step 3: performing Fourier transform on the updated intensity point spread function image I _pr (x, y; n) and the blurred intensity image I (x, y; n) respectively;

3.1 Perform Fourier transform on the updated intensity point spread function image I _pr (x, y; n) according to the following formula:

H(u,v;n)=F{I _pr (x,y;n)}

Wherein, F{} is a Fourier transform operator, H(u, v; n) is the spectrum of the updated intensity point spread function image I _pr (x, y; n) after Fourier transform.

3.2 Perform Fourier transform on the acquired blur intensity image I(x, y; n) according to the following formula:

I(u,v;n)=F{I(x,y;n)}

I(u,v;n) is the spectrum of the fuzzy intensity image I(x,y;n) after Fourier transform.

Step 4: Set a random object spectrum information guess O _g (u, v, 1), which is the initial value of the object guess spectrum value;

Step 5: According to the guessed spectrum value O _g (u, v; n) of the object and the updated intensity point spread function image I _pr (x, y; n) obtained in step 3, the spectrum after Fourier transform represents the guessed Spectrum _Ig (u,v;n) of blurred image:

I _g (u, v; n) = O _g (u, v; n) H (u, v; n)

Step 6: Calculate the difference I _d ( _x ,y;n):

I _d (u, v; n) = I _g (u, v; n) - I (u, v; n)

Step 7: Update the guessed object spectrum information according to the difference calculated in step 6, and get the guessed spectrum value O _g ′(u,v;n) after iterative update:

where H ^* (u,v;n) represents the complex conjugate function of the spectrum H(u,v;n) of the intensity point spread function, and ξ represents the regularization parameter.

Step 8: Assign the updated guessed spectrum value O _g ^′ (u, v; n) to O _g (u, v, n+1) to obtain the updated spectrum information of the object;

Step 9: Calculate the mean value E(u,v;n) and variance σ(u,v;n) of the guessed spectral value O _g ′(u,v;n) of the object after each update iteration, and introduce the method based on the tamura coefficient Focus Criteria (TC):

Step ten: set the critical condition n ₀ , when the TC calculated in step nine is greater than the set threshold n ₀ , output the spectrum of the object at this time as O _g ′(u,v;k), k≤n.

Step 11: Perform inverse Fourier transform on O _g ′(u, v; k), and obtain the reconstructed object image as: o(x, y)=F ^-1 {O _g ′(u, v; k)}, where F ^-1 {} is the inverse Fourier transform operator.

It should be noted that the critical conditions in the above step 9 can be set according to the reconstruction accuracy requirements, or step 9 to step 11 can be omitted, when the collected n pictures are executed from step 3 to step 8, until all intensity Both the point spread function image and the blur intensity image are used to calculate the guessed spectral value O _g ′(u,v;n) of the object after the nth update, and perform Fourier inverse transform on it, that is, the reconstructed object image o(x ,y).

In the embodiment of the present application, only 5 fuzzy images have been collected in the aforementioned steps, so steps 3 to 8 are performed on the 5 collected images to obtain the guessed spectrum value of the object after the 5th update O _g '(u , v; 5), and then the reconstructed object image is obtained by inverse Fourier transform, as shown in Figure 5, and compared with Figure 4, it can be seen that through the method of the present application, the reconstructed image is compared with the aforementioned five blurred images collected. The structural precision is high, which can meet the daily precision requirements.

Part of the steps in the embodiments of the present invention can be realized by software, and the corresponding software program can be stored in a readable storage medium, such as an optical disk or a hard disk.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection of the present invention. within range.

Claims (10)

1. A portable imaging device for penetrating turbid media is characterized by comprising an intelligent terminal, a card slot and a laser; the intelligent terminal has a photographing function, and the clamping groove is used for fixing the laser on the intelligent terminal, so that the intelligent terminal can acquire images under laser irradiation.

2. The apparatus of claim 1, wherein the smart terminal comprises: smart phones, palm computers, and smart watches.

3. The apparatus of claim 1, wherein the laser operates in a wavelength range of 390nm to 780nm with a beam diameter<10mm, beam divergence angle<0.5mrad, line width<1.0cm ^-1 。

4. A portable method for imaging through turbid media, the method being implemented on the basis of the device according to any one of claims 1-3, the method comprising:

step S1: acquiring n blurred images passing through a turbid medium under laser irradiation, wherein the blurred images are marked as I (x, y; n), and n =1,2,3 \8230;

step S2: respectively separating corresponding intensity point diffusion functions from the acquired n blurred images and updating to obtain updated intensity point diffusion functions I _pr (x,y；n)；

And step S3: to the updated intensity point spread function I _pr (x, y; n) and the blurred image I (x, y; n) are respectively subjected to Fourier transform to obtain respective frequency spectrums which are respectively marked as H (u, v; n) and I (u, v; n);

and step S4: setting a random object frequency spectrum guess value as an initial value O of the object guess frequency spectrum information _g (u,v,1)；

Step S5: guessing frequency spectrum information according to the object and the updated intensity point spread function I obtained in the step S3 _pr The spectrum of (x, y; n) represents the spectrum I of the guessed blurred image _g (u,v；n)；

Step S6: computingThe spectrum I of the guessed blurred image obtained in step S5 _g (u, v; n) and the spectrum I (u, v; n) of the blurred image obtained in step S3 _d (u,v；n)；

Step S7: updating the object guessed frequency spectrum information according to the difference calculated in the step S6 to obtain the object guessed frequency spectrum information after iterative updating, and repeating the steps S5 to S7 until the iteration updating of the acquired n fuzzy images is finished to obtain the object guessed frequency spectrum information O after the final iterative updating _g ' (u, v; n) or obtaining the final iteration updated object guess frequency spectrum information O after meeting the preset condition _g ′(u,v；k)，k≤n；

Step S8: guessing the frequency spectrum value O of the object after the final iteration update _g ' (u, v; n) or O _g ' (u, v; k) is inverse Fourier transformed to obtain a reconstructed object image o (x, y).

5. The method according to claim 4, wherein the step S2 comprises:

2.1 irradiating the laser beam on the bright spot area of the object surface as the image of the intensity point spread function, marked I _p (x,y；n)；

The specific size of the bright spot area is determined according to the beam diameter, the beam divergence angle and the line width of the laser;

2.2: setting a zero matrix I _pr The matrix size is the same as I (x, y; n), zero matrix I _pr Image I (x, y; n) of intensity point spread function for region with the same range size as I (x, y; n) at center position _p (x, y; n) substitution to obtain an updated intensity point spread function image I _pr (x,y；n)。

6. The method according to claim 5, wherein the step S5 comprises:

the guessed spectrum I of the blurred image is obtained according to the following formula _g (u,v；n)：

I _g (u,v；n)＝O _g (u,v；n)H(u,v；n)

Wherein H (u, v; n) = F { I _pr (x, y; n) }, H (u, v; n) is updatedIntensity point spread function image I _pr (x, y; n) spectrum after Fourier transform, F { } is the Fourier transform operator.

7. The method according to claim 6, wherein the step S7 of updating the object guessed spectrum information according to the difference calculated in step S6 to obtain the iteratively updated object guessed spectrum information includes:

calculating the frequency spectrum difference value of the guess image and the real image and updating the guess frequency spectrum information of the object according to the following formula:

I _d (u,v；n)＝I _g (u,v；n)-I(u,v；n)

wherein H ^* (u, v; n) represents the complex conjugate function of the spectrum H (u, v; n) of the intensity point spread function, ξ represents the regularization parameter.

8. The method according to claim 7, wherein the step S7 is repeated from step S5 to step S7 until the iteration update of the acquired n blurred images is completed to obtain the final iteration updated object guess frequency spectrum information O _g ' (u, v; n), the object-guessed spectrum information obtained by the previous blurred image iterative update is used as a basis for calculating the spectrum of the guessed blurred image at the next blurred image iterative update.

9. The method according to claim 7, wherein the preset condition in step S7 is that the reconstructed image satisfies a predetermined precision, and the determining method includes:

calculating object guess frequency spectrum information O after each update iteration _g ' (u, v; n) and variance σ (u, v; n), introducing a focusing criterion TC based on tamura coefficients:

setting a critical condition n ₀ When the calculated TC is greater than n ₀ Then, the guessed spectrum information O of the object at the moment is output _g ′(u,v；k)，k≤n。

10. The method according to claim 4, wherein the turbid medium comprises mist, water, milk solution, blood and skin tissue.

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