CN105141857B - Image processing method and device - Google Patents

Abstract

The embodiment of the invention discloses a kind of image processing method and devices.Described image processing method the following steps are included: A, during dark-state scene is imaged, sensitivity is promoted to predetermined value, wherein the predetermined value is in preset range；B, the first imaging data of the dark-state scene is received；C, first imaging data is carried out dropping hot-tempered processing, and exports the second imaging data by noise reduction process.The present invention can promote the imaging effect of the image of dark-state scene.

Description

Image processing method and device

【Technical field】

The invention relates to the field of computer technology, in particular to an image processing method and device.

【Background technique】

In the traditional imaging technology (image processing technology), the technical solution adopted for the insufficient illumination of the night scene is: increase the sensitivity. The lower the brightness of the scene, the higher the sensitivity will be. For some very dark scenes, ISO even reaches 3600.

In practice, the inventors found that the prior art has at least the following problems:

This will increase the gain and at the same time amplify the noise, especially the color noise.

Therefore, the above-mentioned traditional technical solutions are not conducive to improving the quality of details of night scene images.

【Content of invention】

The object of the present invention is to provide an image processing method and device, which can improve the imaging effect of images of dark scenes.

In order to solve the above problems, the technical solution of the present invention is as follows:

An image processing method, the method comprising the following steps: A. in the process of imaging a dark scene, increasing the sensitivity to a predetermined value, wherein the predetermined value is within a predetermined range; B. receiving the The first imaging data of a dark scene; C, performing noise reduction processing on the first imaging data, and outputting second imaging data subjected to noise reduction processing.

In the above image processing method, before the step A, the method further includes the following steps: D, judging the brightness of the dark scene according to the imaging data, and generating a brightness judging result; the step A includes: a1. Raise the photosensitivity to the predetermined value according to the brightness judgment result.

In the above image processing method, the predetermined value includes a first value; the step a1 includes: a11, when the brightness judgment result is that the brightness value of the dark scene is within the first range, the The sensitivity is increased to the first numerical value.

In the above image processing method, the step C includes: c1, identifying noise points from the image corresponding to the first imaging data; c2, using pixels close to the noise points to process the noise points Blurring to blur the noise or remove the noise.

In the above image processing method, the predetermined value includes a second value; the step a1 includes: a12, when the brightness judgment result is that the brightness value of the dark scene is within the second range, the The sensitivity is raised to the second value; after the step B, and before the step C, the method further includes the following steps: E, reducing the saturation value corresponding to the first imaging data to a third value to reduce color noise in the image corresponding to the first imaging data.

An image processing device, the device comprising: a sensitivity control module, configured to raise the sensitivity to a predetermined value during imaging of a dark scene, wherein the predetermined value is within a predetermined range; the imaging data The receiving module is configured to receive the first imaging data of the dark scene; the noise reduction processing module is configured to perform noise reduction processing on the first imaging data, and output second imaging data that has undergone noise reduction processing.

In the above image processing device, the device further includes: a brightness judgment module, configured to judge the brightness of the dark scene according to the imaging data, and generate a brightness judgment result; the sensitivity control module is also configured to judge the brightness according to the Raise the sensitivity to the predetermined value based on the brightness judgment result.

In the above image processing device, the predetermined value includes a first value; the sensitivity control module is further configured to set The photosensitivity is increased to the first numerical value.

In the above image processing device, the noise reduction processing module includes: a noise point identification module, configured to identify noise points from the image corresponding to the first imaging data; a noise point blurring processing module, configured to utilize The pixels of the noise point are used to blur the noise point, so as to blur the noise point or eliminate the noise point.

In the above image processing device, the predetermined value includes a second value; the sensitivity control module is further configured to set The sensitivity is increased to the second value; the device further includes: a saturation control module, configured to reduce the saturation value corresponding to the first imaging data to a third value, so as to reduce the first Color noise in the image corresponding to the imaging data.

Compared with the prior art, the present invention can improve the imaging effect of images of dark scenes.

In order to make the above content of the present invention more obvious and understandable, the preferred embodiments are specifically cited below, and in conjunction with the accompanying drawings, the detailed description is as follows:

【Description of drawings】

Fig. 1 is the block diagram of image processing method of the present invention;

Fig. 2 is a block diagram of the noise reduction processing module in Fig. 1;

Fig. 3 is the flowchart of the image processing device of the present invention;

FIG. 4 is a flow chart of the steps of performing noise reduction processing on the first imaging data in FIG. 3 .

【Detailed ways】

The following descriptions of the various embodiments refer to the accompanying drawings to illustrate specific embodiments in which the present invention can be practiced.

The image processing method and device of the present invention can run on a computer or a user device installed with an operating system platform of a currently commonly used portable and mobile smart device, and the computer can be one or more than one of a personal computer, a server, etc. A combined system, the user equipment may be any one of a mobile terminal, a palmtop computer, a tablet computer, and the like. The computer or the user equipment may include any combination of processors, memory, sensors, switching devices, power supplies, clock signal generators, input and output devices, etc., the processors, memory, sensors, switches in the above computers or user equipment Any combination of devices, power supplies, clock signal generators, input and output devices, etc. is used to realize the steps in the image processing method and the functions in the image processing device of the present invention.

The image processing device and/or method of the present invention can be applied to devices with shooting functions such as mobile phones, cameras, and video cameras.

Referring to FIG. 1 and FIG. 2 , FIG. 1 is a block diagram of the image processing method of the present invention, and FIG. 2 is a block diagram of the noise reduction processing module 105 in FIG. 1 .

The image processing device of the present invention includes a sensitivity control module 102 , an imaging data receiving module 103 and a noise reduction processing module 105 .

The sensitivity control module 102 is configured to increase the sensitivity (ISO) to a predetermined value during imaging of a dark scene, wherein the predetermined value is within a predetermined range. The dark scene may be, for example, a dark scene such as a night scene or a dark indoor scene.

The imaging data receiving module 103 is configured to receive the first imaging data of the dark scene. For example, the imaging data receiving module 103 is configured to receive the first imaging data from an image sensor.

The noise reduction processing module 105 is used for performing noise reduction processing on the first imaging data, and outputting the second imaging data after noise reduction processing.

Wherein, the noise reduction processing module 105 performs noise reduction processing on the first imaging data to improve the detail quality of the captured image.

As an improvement, the device further includes a brightness judging module 101 . The brightness judgment module 101 is configured to judge the brightness of the dark scene according to the imaging data, and generate a brightness judgment result. The sensitivity control module 102 is further configured to increase the sensitivity to the predetermined value according to the brightness judgment result. Since the brightness of different scenes is different, when shooting a scene with different brightness (dark state scene), the brightness judging module 101 judges the brightness of the scene, so that the sensitivity control module 102 can This adjusts the sensitivity in a targeted manner (for example, increasing the sensitivity to the predetermined value, or decreasing the sensitivity to the predetermined value).

Further, the predetermined value includes a first numerical value. The sensitivity control module 102 is further configured to increase the sensitivity to the first value when the brightness judgment result is that the brightness value of the dark scene is within a first range. Wherein, the first value is a preset value of the first balance point, for example, 2400. The sensitivity control module 102 increases the sensitivity to the first value to suppress (or reduce, reduce) the appearance of color noise.

As an improvement, the noise reduction processing module 105 includes a noise point identification module 202 and a noise point blurring processing module 203 . Wherein, the noise point identification module 202 is used for identifying noise points from the image corresponding to the first imaging data. The noise point blurring processing module 203 is used for blurring the noise point by utilizing the pixels close to the noise point, so as to blur the noise point or eliminate the noise point.

Specifically, the noise recognition module 202 is configured to select multiple frames of auxiliary images from the first imaging data, so as to accurately find out the noise in the target image by using the noise information in multiple frames of the auxiliary images. point. The noise point blurring processing module 203 is configured to use pixels close to the noise point in the target image to perform blurring processing on the noise point, so as to blur the noise point or eliminate the noise point.

Wherein, the auxiliary image is a small-size image, and the size of the small-size image is smaller than the size of the target image, which is beneficial to speed up the noise point identification module 202 to identify the noise point.

Preferably, the noise reduction processing module 105 further includes an auxiliary image cropping module 201, and the auxiliary image cropping module 201 is configured to crop at least one of the auxiliary images into the small-sized image.

Further, the predetermined value includes a second numerical value. The sensitivity control module 102 is further configured to increase the sensitivity to the second value when the brightness judgment result is that the brightness value of the dark scene is within a second range. Wherein, the second value is a preset value of the second balance point, for example, 3600. The photosensitivity control module 102 increases the photosensitivity to the second value to adapt to scenes with too low brightness, so that images of the scenes can be successfully generated.

The apparatus also includes a saturation control module 104 . The saturation control module 104 is configured to reduce the saturation value corresponding to the first imaging data to a third value, so as to reduce color noise in the image corresponding to the first imaging data. Wherein, the saturation control module 104 reduces the saturation value to weaken the color noise (so that the color noise is not obvious), so as to improve the imaging effect.

Referring to FIG. 3 and FIG. 4 , FIG. 3 is a flow chart of the image processing device of the present invention, and FIG. 4 is a flow chart of the steps of performing noise reduction processing on the first imaging data in FIG. 3 .

Image processing method of the present invention comprises the following steps:

A. In the process of imaging a dark scene, the sensitivity control module 102 increases the sensitivity to a predetermined value, wherein the predetermined value is within a predetermined range. The dark scene may be, for example, a dark scene such as a night scene or a dark indoor scene.

B. The imaging data receiving module 103 receives the first imaging data of the dark scene. For example, the imaging data receiving module 103 receives the first imaging data from an image sensor.

C. The noise reduction processing module 105 performs noise reduction processing on the first imaging data, and outputs second imaging data subjected to noise reduction processing.

Wherein, the noise reduction processing module 105 performs noise reduction processing on the first imaging data to improve the detail quality of the captured image.

As an improvement, before the step A, the method also includes the following steps:

D. The brightness judgment module 101 judges the brightness of the dark scene according to the imaging data, and generates a brightness judgment result. Wherein, the brightness judgment result includes at least two cases, the first case is that the brightness value corresponding to the brightness is within the first range, and the second case is that the brightness value is within the second range. The first range and the second range may be set according to pre-measured data.

Wherein, the step A includes:

a1. The sensitivity control module 102 increases the sensitivity to the predetermined value according to the brightness judgment result.

Since the brightness of different scenes is different, when shooting a scene with different brightness (dark state scene), the brightness judging module 101 judges the brightness of the scene, so that the sensitivity control module 102 can This adjusts the sensitivity in a targeted manner (for example, increasing the sensitivity to the predetermined value, or decreasing the sensitivity to the predetermined value).

Further, the predetermined value includes a first numerical value. Wherein, the step a1 includes:

a11. The sensitivity control module 102 increases the sensitivity to the first value when the brightness judgment result is that the brightness value of the dark scene is within the first range.

Wherein, the first value is a preset value of the first balance point, for example, 2400. The sensitivity control module 102 increases the sensitivity to the first value to suppress (or reduce, reduce) the appearance of color noise.

As an improvement, the step C includes:

c1. The noise point identification module 202 identifies noise points from the image corresponding to the first imaging data.

c2. The noisy point blurring processing module 203 uses pixels close to the noisy point to perform blurring processing on the noisy point, so as to blur the noise point or eliminate the noise point.

Specifically, the step c1 includes:

c11. The noise spot identification module 202 selects multiple frames of auxiliary images from the first imaging data, so as to accurately find out the noise points in the target image by using the noise information in the multiple frames of the auxiliary images.

Said step c2 comprises:

c2. The noise point blurring processing module 203 uses pixels close to the noise point in the target image to perform blurring processing on the noise point, so as to blur the noise point or eliminate the noise point.

Wherein, the auxiliary image is a small-size image, and the size of the small-size image is smaller than the size of the target image, which is beneficial to speed up the noise point identification module 202 to identify the noise point.

Preferably, said step C also includes:

c3. The auxiliary image cropping module 201 in the noise reduction processing module 105 crops at least one of the auxiliary images into the small-size image.

As an improvement, the predetermined value includes a second numerical value. Wherein, the step a1 includes:

a12. The sensitivity control module 102 increases the sensitivity to the second value when the brightness judgment result is that the brightness value of the dark scene is within the second range. Wherein, the second value is a preset value of the second balance point, for example, 3600. The photosensitivity control module 102 increases the photosensitivity to the second value to adapt to scenes with too low brightness, so that images of the scenes can be successfully generated.

After said step B, and before said step C, said method also includes the following steps:

E. The saturation control module 104 reduces the saturation value corresponding to the first imaging data to a third value, so as to reduce color noise in the image corresponding to the first imaging data. Wherein, the saturation control module 104 reduces the saturation value to weaken the color noise (so that the color noise is not obvious), so as to improve the imaging effect.

Through the above technical solution, it is beneficial to improve the imaging effect of the image of the dark scene.

Example

In the process of imaging the dark scene, the brightness judgment module 101 judges the brightness of the dark scene, and when the brightness judgment result is that the brightness value of the dark scene is within the first range, The photosensitivity control module 102 does not raise the photosensitivity too high, and only pulls it to 2400 at the maximum. At this time, the color noise of the first imaging data of the dark scene will be effectively controlled.

In the case of relatively large suppression of the color noise, further processing of the imaging noise is required for better imaging. The corresponding technical solutions are:

Choose the three-party multi-frame synthesis method to further resist the noise, improve the contrast of the imaging, and improve the imaging effect.

If the brightness of some scenes is very dark, for example, if the brightness judgment result is that the brightness value of the dark scene is within the second range, the imaging needs to be made with a sensitivity of more than 3600, and further processing is required at this time. In order to suppress the color noise, it is necessary to reduce the saturation of the entire scene, so that the color noise will become less obvious, which is conducive to improving the imaging effect.

While the invention has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art upon the reading and understanding of this specification and the annexed drawings. The present invention includes all such modifications and variations and is limited only by the scope of the appended claims. With particular regard to the various functions performed by the above-described components (eg, elements, resources, etc.), terminology used to describe such components is intended to correspond to Any component (unless otherwise indicated), even if not structurally equivalent to the disclosed structure that performs the function in the exemplary implementations of the specification presented herein. Furthermore, although a particular feature of this specification has been disclosed with respect to only one of several implementations, such feature may be combined with one or more other implementations as may be desirable and advantageous for a given or particular application. other feature combinations. Moreover, to the extent the terms "comprises", "has", "comprising" or variations thereof are used in the detailed description or the claims, such terms are intended to be encompassed in a manner similar to the term "comprising".

In summary, although the present invention has been disclosed above with preferred embodiments, the above preferred embodiments are not intended to limit the present invention, and those of ordinary skill in the art can make various modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be determined by the scope defined in the claims.

Claims (2)

1. An image processing method, characterized in that the method comprises the following steps: D. Judging the brightness of the dark scene according to the imaging data, and generating a brightness judgment result; A. In the process of imaging a dark scene, according to the brightness judgment result, increase the sensitivity to a predetermined value, wherein the predetermined value is within a predetermined range, and the predetermined value includes the first value and the second value. numerical value, if the brightness judgment result is that the brightness value of the dark state scene is within the first range, increase the sensitivity to the first numerical value, and if the brightness judgment result is the dark state scene When the brightness value of the scene is within the second range, increasing the sensitivity to the second value; B. receiving the first imaging data of the dark scene; E. Decreasing the saturation value corresponding to the first imaging data to a third value, so as to reduce color noise in the image corresponding to the first imaging data; C. Perform noise reduction processing on the first imaging data, and output second imaging data that has undergone noise reduction processing, including: identifying noise points from the image corresponding to the first imaging data, and using The noise point is blurred by the pixels of the point, so as to blur the noise point or eliminate the noise point, wherein a plurality of frames of auxiliary images are selected from the first imaging data to utilize the multi-frame The noise point information in the auxiliary image finds the noise point in the target image, the auxiliary image is a small-size image, and the size of the small-size image is smaller than the size of the target image. 2. An image processing device, characterized in that the device comprises: Brightness judging module, used for judging the brightness of the dark state scene according to the imaging data, and generating the brightness judging result; The photosensitivity control module is configured to increase the photosensitivity to a predetermined value according to the brightness judgment result in the process of imaging a dark scene, wherein the predetermined value is within a predetermined range, and the predetermined value includes the first a numerical value and a second numerical value, increasing the sensitivity to the first numerical value, and increasing the sensitivity to to said second value; An imaging data receiving module, configured to receive the first imaging data of the dark scene; A saturation control module, configured to reduce the saturation value corresponding to the first imaging data to a third value, so as to reduce color noise in the image corresponding to the first imaging data; A noise reduction processing module, configured to perform noise reduction processing on the first imaging data, and output the second imaging data after noise reduction processing, the noise reduction processing module includes a noise point identification module, and the noise point identification module uses Selecting multiple frames of auxiliary images from the first imaging data to find noise points in the target image by using the noise information in the multiple frames of the auxiliary images, the auxiliary images are small-sized images, and the small The size of the size image is smaller than the size of the target image, and the noise reduction processing module includes: A noise point identification module, configured to identify noise points from the image corresponding to the first imaging data; The noise point blurring processing module is used to perform blurring processing on the noise point by using pixel points close to the noise point, so as to blur the noise point or eliminate the noise point.