CN110519485B - Image processing method, image processing device, storage medium and electronic equipment - Google Patents
Image processing method, image processing device, storage medium and electronic equipment Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/70—Circuitry for compensating brightness variation in the scene
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/70—Circuitry for compensating brightness variation in the scene
- H04N23/71—Circuitry for evaluating the brightness variation
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/70—Circuitry for compensating brightness variation in the scene
- H04N23/73—Circuitry for compensating brightness variation in the scene by influencing the exposure time
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/70—Circuitry for compensating brightness variation in the scene
- H04N23/76—Circuitry for compensating brightness variation in the scene by influencing the image signals
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/80—Camera processing pipelines; Components thereof
- H04N23/81—Camera processing pipelines; Components thereof for suppressing or minimising disturbance in the image signal generation
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Abstract
The application discloses an image processing method, an image processing device, a storage medium and an electronic device. The image processing method comprises the following steps: carrying out brightness enhancement processing before exposure and acquiring basic image data after exposure, or carrying out brightness enhancement processing on the image data obtained after exposure to obtain basic image data; carrying out noise reduction processing on the basic image data to obtain noise reduction image data; and carrying out contrast-limited adaptive histogram equalization processing on the noise-reduced image data to obtain a target image. The image processing method provided by the embodiment of the application is beneficial to the electronic equipment to accurately extract the image characteristics from the image.
Description
Technical Field
The present application belongs to the field of image technologies, and in particular, to an image processing method, an image processing apparatus, a storage medium, and an electronic device.
Background
Image processing techniques often require image feature extraction. The features of the image mainly include color features, texture features, shape features, spatial relationship features and the like of the image. The image characteristics have very important significance for image processing. If the electronic device can accurately extract features from an image, the electronic device can efficiently and accurately perform image processing. However, in the related art, the accuracy of extracting features from an image by an electronic device is low.
Disclosure of Invention
The embodiment of the application provides an image processing method, an image processing device, a storage medium and electronic equipment, which are beneficial for the electronic equipment to accurately extract image features from an image.
In a first aspect, an embodiment of the present application provides an image processing method, including:
carrying out brightness enhancement processing before exposure and acquiring basic image data after exposure, or carrying out brightness enhancement processing on the image data obtained after exposure to obtain basic image data;
carrying out noise reduction processing on the basic image data to obtain noise reduction image data;
and carrying out contrast-limited adaptive histogram equalization processing on the noise-reduced image data to obtain a target image.
In a second aspect, an embodiment of the present application provides an image processing apparatus, including:
the first processing module is used for carrying out brightness improvement processing before exposure and acquiring basic image data after exposure, or carrying out brightness improvement processing on the image data obtained after exposure to obtain the basic image data;
the noise reduction module is used for carrying out noise reduction processing on the basic image data to obtain noise reduction image data;
and the second processing module is used for carrying out contrast-limited adaptive histogram equalization processing on the noise-reduced image data to obtain a target image.
In a third aspect, an embodiment of the present application provides a storage medium, on which a computer program is stored, which, when executed on a computer, causes the computer to execute a flow in an image processing method provided by an embodiment of the present application.
In a fourth aspect, an embodiment of the present application further provides an electronic device, which includes a memory and a processor, where the processor is configured to execute the flow in the image processing method provided in the embodiment of the present application by calling a computer program stored in the memory.
According to the method and the device, on one hand, texture features in the image are clearer due to the fact that the target image is subjected to brightness improvement and noise reduction, and on the other hand, gray scale differences of the image are amplified due to the fact that the target image is subjected to contrast limited adaptive histogram equalization processing, so that image feature points are clearer. Therefore, the feature point detection of the target image becomes easier and more accurate. That is, the present embodiment facilitates the electronic device to accurately extract image features from an image.
Drawings
The technical solutions and advantages of the present application will become apparent from the following detailed description of specific embodiments of the present application when taken in conjunction with the accompanying drawings.
Fig. 1 is a schematic flowchart of a first image processing method according to an embodiment of the present application.
Fig. 2 is a schematic flowchart of a second image processing method according to an embodiment of the present application.
Fig. 3 is a schematic flowchart of a third image processing method according to an embodiment of the present application.
Fig. 4 is a fourth flowchart illustrating an image processing method according to an embodiment of the present application.
Fig. 5 is a fifth flowchart illustrating an image processing method according to an embodiment of the present application.
Fig. 6 to fig. 8 are scene schematic diagrams of an image processing method according to an embodiment of the present application.
Fig. 9 is a schematic structural diagram of an image processing apparatus according to an embodiment of the present application.
Fig. 10 is a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Fig. 11 is another schematic structural diagram of an electronic device according to an embodiment of the present application.
Fig. 12 is a schematic structural diagram of an image processing circuit according to an embodiment of the present application.
Detailed Description
Referring to the drawings, wherein like reference numbers refer to like elements, the principles of the present application are illustrated as being implemented in a suitable computing environment. The following description is based on illustrated embodiments of the application and should not be taken as limiting the application with respect to other embodiments that are not detailed herein.
It is understood that the execution subject of the embodiment of the present application may be an electronic device such as a smart phone or a tablet computer.
Referring to fig. 1, fig. 1 is a first schematic flow chart of an image processing method according to an embodiment of the present application, where the flow chart may include:
101. and carrying out brightness enhancement processing before exposure and acquiring the basic image data after exposure, or carrying out brightness enhancement processing on the image data obtained after exposure to obtain the basic image data.
Image processing techniques often require image feature extraction. The features of the image mainly include color features, texture features, shape features, spatial relationship features and the like of the image. The image characteristics have very important significance for image processing. If the electronic device can accurately extract features from an image, the electronic device can efficiently and accurately perform image processing. However, in the related art, the accuracy of extracting features from an image by an electronic device is low.
In this embodiment, for example, the electronic device may perform brightness enhancement processing before exposure, and acquire image data obtained by exposure (i.e., base image data). Or, the electronic device may normally perform exposure and acquire image data obtained after the exposure is completed, and then perform brightness enhancement processing on the image data obtained after the exposure is completed, thereby obtaining basic image data. That is, compared to an image obtained by performing normal exposure according to the current shooting environment, in 101, the electronic device may acquire image data with improved image brightness.
For example, the electronic device calculates an exposure parameter a according to the current shooting environment, and if normal exposure is performed according to the exposure parameter a, the first image data can be obtained. In the embodiment of the present application, the electronic device may adjust the exposure parameter a before exposure, for example, the adjusted exposure parameter is B. The electronic device performs exposure according to the exposure parameter B to obtain second image data (the second image data is the basic image data). And the brightness of the image corresponding to the second image data is greater than that of the image corresponding to the first image data.
Or, the electronic device may also perform exposure according to the exposure parameter a to obtain first image data, and then perform brightness enhancement processing on the first image data to obtain third image data (the third image data is basic image data), where brightness of an image corresponding to the third image data is greater than brightness of an image corresponding to the first image data.
That is, in this embodiment, two ways may be adopted to acquire the image data with relatively improved brightness, and the first way is to relatively improve the brightness of the image data obtained by exposure by adjusting the exposure parameters before exposure. The second method is to perform brightness enhancement processing on image data obtained after exposure is completed, thereby enhancing the brightness of an image.
In other embodiments, the electronic device may perform the brightness enhancement processing by increasing the exposure time period before the exposure.
It should be noted that, in this embodiment, compared to the image data obtained by normal exposure, the luminance of the base image data is increased, and the texture features and the like in the image can be made clearer by increasing the luminance of the image. That is, the texture features in the image corresponding to the second image data or the third image data may be clearer than the texture features in the image corresponding to the first image data.
102. And carrying out noise reduction processing on the basic image data to obtain noise-reduced image data.
For example, after obtaining the basic image data with relatively improved brightness, the electronic device may perform noise reduction processing on the basic image data, so as to obtain noise-reduced image data.
It should be noted that, in this embodiment 101, noise in the image is increased while the image brightness is increased. Accordingly, the electronic device may perform noise reduction processing on the base image data at 102.
103. And carrying out contrast-limited adaptive histogram equalization processing on the noise-reduced image data to obtain a target image.
For example, after obtaining the noise-reduced image data, the electronic device may perform Contrast-Limited Adaptive histogram equalization (CLAHE) processing on the noise-reduced image data, so as to obtain the target image.
It should be noted that, in this embodiment, the contrast of the image may be improved by performing contrast-limited adaptive histogram equalization processing on the noise-reduced image data, so as to further amplify the grayscale difference of the image. It is understood that the feature points in the target image can be made clearer by enlarging the gray scale difference of the image.
It should also be noted that, in the present embodiment, the contrast ratio of the image is improved by using the contrast-limited adaptive histogram equalization CLAHE process, so that the gray scale of the image can be stretched to a proper degree without causing serious distortion of the image. If Adaptive Histogram Equalization (AHE) is used to improve the contrast of an image, the gray scale of the image is easily over-stretched, which causes severe distortion of the image and further causes severe distortion of the feature points in the image.
It can be understood that the target image obtained by the embodiment of the application has clearer texture features in the image due to the fact that the target image is subjected to brightness improvement and noise reduction processing, and has clearer image feature points due to the fact that the target image is subjected to contrast-limited adaptive histogram equalization processing so that gray scale differences of the image are amplified. Therefore, the feature point detection of the target image becomes easier and more accurate. That is, the present embodiment facilitates the electronic device to accurately extract image features from an image.
Referring to fig. 2, fig. 2 is a schematic flow chart of an image processing method according to an embodiment of the present application, where the flow chart may include:
201. the electronic device acquires an ambient light brightness value.
For example, when capturing an image, the electronic device may first obtain an ambient light brightness value of a current captured scene.
Thereafter, the electronic device may detect whether the ambient light brightness value is less than a preset brightness threshold. For example, the value of the preset luminance threshold may be set to be relatively small.
If the ambient light brightness value is detected to be greater than or equal to the preset brightness threshold value, the current shooting scene can be considered as the shooting scene under the normal light brightness. In this case, the electronic device may calculate an exposure parameter according to an environmental parameter (e.g., an environmental brightness, etc.) of the shooting scene, and perform a normal exposure according to the exposure parameter, thereby obtaining a desired image.
If the ambient light brightness value is detected to be smaller than the preset brightness threshold, the process proceeds to 202.
202. If the detected ambient light brightness value is smaller than the preset brightness threshold value, the electronic equipment gains the light sensitivity before exposure so as to carry out brightness improvement processing, and basic image data after exposure are obtained.
For example, when the electronic device detects that the ambient light brightness value of the current shooting scene is smaller than the preset brightness threshold, the current shooting scene may be considered as a shooting scene with dark light, such as a night scene shooting scene with dark light. In this case, the electronic device may gain sensitivity before exposure so as to increase brightness of an image obtained after completion of the exposure, and determine image data obtained after completion of the exposure as base image data.
It should be noted that, in this embodiment, compared to an image obtained by normal exposure, the luminance of the image corresponding to the base image data is increased, and the texture features and the like in the image corresponding to the base image data can be made clearer by increasing the luminance of the image.
203. And the electronic equipment performs noise reduction processing on the basic image data in a Gaussian filtering or bilateral filtering mode to obtain noise reduction image data.
For example, after obtaining the base image data, the electronic device may perform noise reduction processing on the base image data in a gaussian filtering or bilateral filtering manner to obtain noise-reduced image data.
Of course, in other embodiments, the electronic device may also perform noise reduction processing on the base image data by using other image noise reduction methods, which is not limited in this embodiment.
It should be noted that noise in the image is increased while the brightness of the image is increased. Accordingly, the electronic device may perform noise reduction processing on the base image data at 203, thereby reducing noise.
204. And the electronic equipment performs contrast-limited adaptive histogram equalization processing on the noise-reduced image data to obtain a target image.
For example, after obtaining the noise-reduced image data, the electronic device may perform Contrast-Limited Adaptive histogram equalization (CLAHE) processing on the noise-reduced image data, so as to obtain the target image.
It should be noted that, in this embodiment, performing contrast-limited adaptive histogram equalization processing on image data may improve the contrast of an image, so as to further amplify the grayscale difference of the image. It is understood that the feature points in the image can be made clearer by enlarging the gray scale difference of the image.
It is understood that when the image is in a dark shot scene (especially a very dark scene), if the exposure is performed in a normal exposure manner, the texture detail of the image obtained by the normal exposure is weak, even weak to be difficult to distinguish. In addition, in a dark shooting scene (particularly, an extremely dark scene), noise is relatively large in an image, and when image feature extraction is performed, the noise is easily regarded as a feature point.
In this embodiment, when the electronic device is in a shooting scene with dark light, the electronic device can make texture features in an image clearer in a mode of improving image brightness, and noise caused by improving image brightness can be reduced in a noise reduction mode. Moreover, the gray scale difference of the image can be amplified through the contrast-limited adaptive histogram equalization processing, so that the image characteristic points become clearer. Therefore, the feature point detection of the target image becomes easier and more accurate. That is, the present embodiment is advantageous for the electronic device to accurately extract image features from an image captured in a dark scene.
Referring to fig. 3, fig. 3 is a third schematic flow chart of an image processing method according to an embodiment of the present application, where the flow chart may include:
301. the electronic device acquires an ambient light brightness value.
For example, when capturing an image, the electronic device may first obtain an ambient light brightness value of a current captured scene.
Thereafter, the electronic device may detect whether the ambient light brightness value is less than a preset brightness threshold. For example, the value of the preset luminance threshold may be set to be relatively small.
If the ambient light brightness value is detected to be greater than or equal to the preset brightness threshold value, the current shooting scene can be considered as the shooting scene under the normal light brightness. In this case, the electronic device may calculate an exposure parameter according to an environmental parameter (e.g., an environmental brightness, etc.) of the shooting scene, and perform a normal exposure according to the exposure parameter, thereby obtaining a desired image.
If the ambient light brightness value is detected to be smaller than the preset brightness threshold, the process proceeds to 302.
302. If the detected ambient light brightness value is smaller than the preset brightness threshold, the electronic device obtains a first sensitivity before exposure, wherein the first sensitivity is a sensitivity for exposure calculated by the electronic device according to a shooting environment.
For example, when the electronic device detects that the ambient light brightness value of the current shooting scene is smaller than the preset brightness threshold, the current shooting scene may be considered as a shooting scene with dark light, such as a night scene shooting scene with dark light. In this case, the electronic apparatus may acquire the first sensitivity before exposure, wherein the first sensitivity is a sensitivity for exposure calculated by the electronic apparatus according to the current shooting environment.
303. The electronic device obtains a gain factor.
304. The electronic equipment gains the first sensitivity according to the gain coefficient to obtain a second sensitivity, wherein the second sensitivity is greater than the first sensitivity.
For example, 303 and 304 may include:
the electronic apparatus may further acquire a gain coefficient, and gain the first sensitivity according to the gain coefficient, thereby obtaining the second sensitivity. Wherein the second sensitivity is greater than the first sensitivity.
For example, the gain factor may have a value of 1.5 or 2 or 3 or 5, etc. The electronic apparatus may determine a product of the first sensitivity and the gain coefficient as the second sensitivity.
305. The electronic device performs exposure according to the second sensitivity and acquires basic image data after exposure.
For example, after the second sensitivity is calculated, the electronic device may perform exposure at the second sensitivity and acquire basic image data obtained after the exposure is completed.
306. And the electronic equipment performs noise reduction processing on the basic image data in a Gaussian filtering or bilateral filtering mode to obtain noise reduction image data.
For example, after obtaining the base image data, the electronic device may perform noise reduction processing on the base image data in a gaussian filtering or bilateral filtering manner to obtain noise-reduced image data.
307. And the electronic equipment performs contrast-limited adaptive histogram equalization processing on the noise-reduced image data to obtain a target image.
For example, after obtaining the noise-reduced image data, the electronic device may perform Contrast-Limited Adaptive histogram equalization (CLAHE) processing on the noise-reduced image data, so as to obtain the target image.
In an embodiment, the process of acquiring, by the electronic device in 303, the gain factor may include:
the electronic equipment acquires an environment light brightness value and acquires a gain coefficient according to the environment light brightness value.
For example, the electronic device may preset a corresponding relationship between the ambient light brightness and the gain coefficient, and different ambient light brightness values or different ambient light brightness intervals may correspond to different gain coefficients. For example, the smaller the ambient light brightness value, the larger its corresponding gain factor may be, and so on.
Then, the electronic device may obtain a corresponding gain factor according to the ambient light brightness value of the current shooting scene.
Referring to fig. 4, fig. 4 is a fourth schematic flow chart of an image processing method according to an embodiment of the present application, where the flow chart may include:
401. the electronic device acquires an ambient light brightness value.
For example, when capturing an image, the electronic device may first obtain an ambient light brightness value of a current captured scene.
Thereafter, the electronic device may detect whether the ambient light brightness value is less than a preset brightness threshold. For example, the value of the preset luminance threshold may be set to be relatively small.
If the ambient light brightness value is detected to be greater than or equal to the preset brightness threshold value, the current shooting scene can be considered as the shooting scene under the normal light brightness. In this case, the electronic device may calculate an exposure parameter according to an environmental parameter (e.g., an environmental brightness, etc.) of the shooting scene, and perform a normal exposure according to the exposure parameter, thereby obtaining a desired image.
If the ambient light brightness value is detected to be smaller than the preset brightness threshold, the process proceeds to 402.
402. If the detected ambient light brightness value is smaller than the preset brightness threshold, the electronic equipment acquires a third sensitivity before exposure, wherein the third sensitivity is a preset sensitivity numerical value.
For example, when the electronic device detects that the ambient light brightness value of the current shooting scene is smaller than the preset brightness threshold, the current shooting scene may be considered as a shooting scene with dark light, such as a night scene shooting scene with dark light. In this case, the electronic apparatus may acquire the third sensitivity before the exposure, wherein the third sensitivity is a sensitivity numerical value set in advance.
403. The electronic device performs exposure using the third sensitivity instead of the first sensitivity, which is a sensitivity for exposure calculated by the electronic device according to a shooting environment, and acquires basic image data after the exposure, the third sensitivity being greater than the first sensitivity.
For example, after acquiring the third sensitivity, the electronic apparatus may perform exposure using the third sensitivity instead of the first sensitivity, and acquire basic image data obtained after the exposure is completed. The first sensitivity is a sensitivity calculated by the electronic device according to a shooting environment and used for exposure, and the value of the third sensitivity can be larger than that of the first sensitivity.
In one embodiment, the value of the third sensitivity may be set to be relatively large and may be significantly larger than the first sensitivity.
That is, when the electronic device is in a dark shooting scene, the electronic device may perform exposure using a preset third sensitivity having a larger value instead of the first sensitivity calculated according to the shooting environment, and acquire image data (i.e., basic image data) obtained after the exposure is completed.
404. And the electronic equipment performs noise reduction processing on the basic image data in a Gaussian filtering or bilateral filtering mode to obtain noise reduction image data.
For example, after obtaining the base image data, the electronic device may perform noise reduction processing on the base image data in a gaussian filtering or bilateral filtering manner to obtain noise-reduced image data.
405. And the electronic equipment performs contrast-limited adaptive histogram equalization processing on the noise-reduced image data to obtain a target image.
For example, after obtaining the noise-reduced image data, the electronic device may perform Contrast-Limited Adaptive histogram equalization (CLAHE) processing on the noise-reduced image data, so as to obtain the target image.
Referring to fig. 5, fig. 5 is a schematic diagram illustrating a fifth flowchart of an image processing method according to an embodiment of the present application, where the flowchart may include:
501. the electronic device acquires an ambient light brightness value.
For example, when capturing an image, the electronic device may first obtain an ambient light brightness value of a current captured scene.
Thereafter, the electronic device may detect whether the ambient light brightness value is less than a preset brightness threshold. For example, the value of the preset luminance threshold may be set to be relatively small.
If the ambient light brightness value is detected to be greater than or equal to the preset brightness threshold value, the current shooting scene can be considered as the shooting scene under the normal light brightness. In this case, the electronic device may calculate an exposure parameter according to an environmental parameter (e.g., an environmental brightness, etc.) of the shooting scene, and perform a normal exposure according to the exposure parameter, thereby obtaining a desired image.
If the ambient light brightness value is detected to be smaller than the preset brightness threshold, the process proceeds to 502.
502. And if the ambient light brightness value is detected to be smaller than the preset brightness threshold value, the electronic equipment performs brightness improvement processing on the image data obtained after exposure to obtain basic image data.
For example, when the electronic device detects that the ambient light brightness value of the current shooting scene is smaller than the preset brightness threshold, the current shooting scene may be considered as a shooting scene with dark light, such as a night scene shooting scene with dark light. In this case, the electronic device may calculate an exposure parameter according to an environmental parameter corresponding to the shooting scene, and normally expose the electronic device according to the exposure parameter to obtain image data.
Then, the electronic device may perform brightness enhancement processing on the image data obtained after the exposure is completed, thereby obtaining basic image data. In this embodiment, the electronic device may use some common ways of increasing the brightness of the image to increase the brightness of the image corresponding to the image data obtained after exposure, for example, linear operation is used to complete brightness increase, for example, the brightness value of all pixels is multiplied or added by a positive enhancement coefficient whose value is greater than 1, so as to increase the brightness of the image. Alternatively, the electronic device may use a method such as tone mapping to increase the brightness of an image corresponding to the image data obtained after the exposure is completed, thereby obtaining the base image data.
In this embodiment, since the brightness enhancement processing is performed on the image data obtained after the exposure is completed, the texture features and the like in the image corresponding to the base image data can be made clearer.
503. And the electronic equipment performs noise reduction processing on the basic image data in a Gaussian filtering or bilateral filtering mode to obtain noise reduction image data.
For example, after obtaining the base image data, the electronic device may perform noise reduction processing on the base image data in a gaussian filtering or bilateral filtering manner to obtain noise-reduced image data.
It should be noted that noise in the image is increased while the brightness of the image is increased. Accordingly, at 503, the electronic device may perform noise reduction processing on the base image data, thereby reducing noise.
504. And the electronic equipment performs contrast-limited adaptive histogram equalization processing on the noise-reduced image data to obtain a target image.
For example, after obtaining the noise-reduced image data, the electronic device may perform Contrast-Limited Adaptive histogram equalization (CLAHE) processing on the noise-reduced image data, so as to obtain the target image.
It should be noted that, in this embodiment, performing contrast-limited adaptive histogram equalization processing on an image may improve the contrast of the image, so as to further amplify the grayscale difference of the image, and thus make the feature points in the image clearer.
In this embodiment, when the electronic device is in a shooting scene with dark light, the electronic device can make texture features in an image clearer in a mode of improving image brightness, and noise caused by improving image brightness can be reduced in a noise reduction mode. Moreover, the gray scale difference of the image can be amplified through the contrast-limited adaptive histogram equalization processing, so that the image characteristic points become clearer. Therefore, the feature point detection of the target image becomes easier and more accurate. That is, the present embodiment facilitates the electronic device to accurately extract image features from an image.
In some embodiments, this embodiment may further include the following process:
the electronic equipment acquires at least two frames of images, wherein the at least two frames of images are images obtained by processing through an image processing method;
the electronic device performs feature point matching-based image registration on the at least two frames of images.
For example, the electronic device may acquire at least two frames of images, where the at least two frames of images are images obtained by the image processing method provided in this embodiment. Thereafter, the electronic device can perform feature point-based image registration processing on the at least two frames of images.
Image registration (Image registration) is a process of matching and superimposing two or more images acquired at different times, different sensors (imaging devices) or under different conditions (weather, illuminance, camera position and angle, etc.), and has been widely used in the fields of remote sensing data analysis, computer vision, Image processing, etc.
The flow of the image registration technique is as follows: taking registration of two frames of images as an example, firstly, extracting features of the two frames of images to obtain feature points; finding matched characteristic point pairs by carrying out similarity measurement; then obtaining image space coordinate transformation parameters through the matched feature point pairs; and finally, carrying out image registration by the coordinate transformation parameters. And the feature extraction is the key in the registration technology, and the accurate feature extraction provides guarantee for the successful implementation of feature matching. Therefore, finding a feature extraction method with good invariance and accuracy is crucial to the matching accuracy.
It can be understood that, since the image processing method provided by the embodiment can make the features in the image clearer, the electronic device can accurately extract the image features from the image. Therefore, the image registration by using the image processed by the image processing method provided by the embodiment has high registration accuracy.
Referring to fig. 6 to 8, fig. 6 to 8 are scene diagrams of an image processing method according to an embodiment of the present application.
For example, a user takes a picture using a camera application in an electronic device. The current shooting scene is a night scene with dark ambient light. As shown in fig. 6, in a dark night scene, when the user clicks the photographing button, the electronic device may acquire an ambient light brightness value and detect whether the ambient light brightness value is smaller than a preset brightness threshold. For example, the preset luminance threshold is a luminance value having a small value. If the ambient light brightness value is smaller than the preset brightness threshold, the electronic device may determine that the current scene is a dark-light shooting scene.
For example, in this embodiment, when the electronic device detects that the current ambient light brightness value is smaller than the preset brightness threshold, the electronic device may determine that the electronic device is currently in a dark light shooting scene. In this case, the electronic apparatus may acquire a preset third sensitivity whose value is set larger. Before exposure, the electronic device may use the preset third sensitivity to perform exposure, so that the brightness of an image obtained after exposure is improved. For example, the electronic device may determine, as the base image, an image obtained after completion of exposure at the preset third sensitivity. In one embodiment, the base image may be a RAW image.
After obtaining the base image, the electronic device may perform noise reduction processing on the base image in a gaussian filtering manner to obtain a noise-reduced image. In one embodiment, the noise-reduced image may also be an image in RAW format.
After obtaining the noise-reduced image, the electronic device may perform contrast-limited adaptive histogram equalization on the noise-reduced image, thereby obtaining a target image.
It can be understood that, in this embodiment, the brightness and the details of the target image obtained through the three image processing of the brightening, the noise reduction, and the contrast-limited adaptive histogram equalization are all greatly improved. For example, if the exposure parameter automatically calculated by the electronic device only from the current shooting environment parameter is a first parameter, the sensitivity in the first parameter is, for example, a first sensitivity. Assume that the image obtained by exposure according to this first parameter is P1. In the embodiment, for example, the image obtained by exposing the electronic device according to the second parameter is P2. The sensitivity in the second parameter may be a preset third sensitivity. The third sensitivity may be substantially greater than the first sensitivity. In one embodiment, the other exposure parameters of the first and second parameters may be the same except for different sensitivities.
Since it is an image taken in a dark night scene, the texture features in the image P1 will be weak and difficult to recognize, and the noise therein will be large. And the image P2 is a target image obtained by three image processing of brightening, noise reduction and contrast-limited adaptive histogram equalization, so that the brightness and the details of the image P2 are greatly improved, and the image features of the image P2 can be more easily and accurately identified. For example, as shown in fig. 7, the electronic apparatus can recognize only the feature point F1 in the image P1. And the electronic device may identify feature points F1, F2, F3, F4, F5 in the image P2.
For example, in a dark night scene, the electronic device may capture three target images, such as P2, P3, and P4, respectively, according to the image processing method provided in this embodiment. After the images P2, P3, and P4 are captured, the electronic device may extract feature points of the respective images. Then, image registration based on feature point matching is performed on the images P2, P3, P4. Although the images P2, P3 and P4 are taken in a dark light environment, the electronic device can relatively easily extract features from the images and perform image registration on the images P2, P3 and P4 because the images P2, P3 and P4 are greatly improved in brightness and detail after being subjected to three processes of brightening, noise reduction and contrast-limited adaptive histogram equalization. That is, the present embodiment can improve the efficiency of image registration.
For example, as shown in fig. 8, the electronic device identifies feature points F1, F2, F3, F4, F5 in image P2, feature points F6, F7, F8, F9, F10 in image P3, feature points F11, F12, F13, F14, F15 in image P4. Where feature points F1, F6, and F11 are matching features, feature points F2, F7, and F12 are matching features, feature points F3, F8, and F13 are matching features, feature points F4, F9, and F14 are matching features, and feature points F5, F10, and F15 are matching features. Then, the electronic device may perform image registration on the images P2, P3, P4 based on these matched feature points. After image registration, the electronic device may fuse the images P2, P3, and P4 to obtain a frame of image with better imaging quality, and display the image as a photograph on a screen for viewing by a user.
Referring to fig. 9, fig. 9 is a schematic structural diagram of an image processing apparatus according to an embodiment of the present disclosure. The image processing apparatus 900 may include: a first processing module 901, a noise reduction module 902 and a second processing module 903.
The first processing module 901 is configured to perform brightness enhancement processing before exposure and obtain basic image data after exposure, or perform brightness enhancement processing on image data obtained after exposure to obtain basic image data.
And a denoising module 902, configured to perform denoising processing on the basic image data to obtain denoised image data.
And a second processing module 903, configured to perform contrast-limited adaptive histogram equalization on the noise-reduced image data to obtain a target image.
In one embodiment, the first processing module 901 may be configured to:
the sensitivity is gained before exposure to perform brightness enhancement processing, and basic image data after exposure is acquired.
In one embodiment, the noise reduction module 902 may be configured to:
and performing noise reduction processing on the basic image data by adopting a Gaussian filtering or bilateral filtering mode to obtain noise reduction image data.
In one embodiment, the first processing module 901 may further be configured to:
acquiring an ambient light brightness value;
if the ambient light brightness value is detected to be smaller than the preset brightness threshold value, brightness improvement processing is carried out before exposure, and exposed basic image data are obtained, or brightness improvement processing is carried out on the exposed image data to obtain the basic image data.
In one embodiment, the first processing module 901 may be configured to:
acquiring first sensitivity before exposure, wherein the first sensitivity is the sensitivity used for exposure calculated by the electronic equipment according to a shooting environment;
obtaining a gain coefficient;
the first sensitivity is gained according to the gain coefficient to obtain a second sensitivity, and the second sensitivity is greater than the first sensitivity;
and carrying out exposure according to the second sensitivity, and acquiring basic image data after exposure.
In one embodiment, the first processing module 901 may be configured to:
acquiring a third sensitivity before exposure, wherein the third sensitivity is a preset sensitivity numerical value;
and replacing the first sensitivity with the third sensitivity to carry out exposure to obtain the basic image data after exposure, wherein the first sensitivity is the sensitivity calculated by the electronic equipment according to the shooting environment and is used for exposure, and the third sensitivity is greater than the first sensitivity.
In one embodiment, the first processing module 901 may be configured to:
and acquiring an environment light brightness value, and acquiring a gain coefficient according to the environment light brightness value.
In one embodiment, the second processing module 903 may further be configured to:
acquiring at least two frames of images, wherein the at least two frames of images are images obtained by processing through the image processing method;
and carrying out image registration based on feature point matching on the at least two frames of images.
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which, when executed on a computer, causes the computer to execute the flow in the image processing method provided by this embodiment.
The embodiment of the present application further provides an electronic device, which includes a memory and a processor, where the processor is configured to execute the flow in the image processing method provided in this embodiment by calling the computer program stored in the memory.
For example, the electronic device may be a mobile terminal such as a tablet computer or a smart phone. Referring to fig. 10, fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
The electronic device 1000 may include components such as a camera module 1001, memory 1002, processor 1003, and the like. Those skilled in the art will appreciate that the electronic device configuration shown in fig. 10 does not constitute a limitation of the electronic device and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
The camera module 1001 may include a lens for collecting an external light source signal and providing the light source signal to the image sensor, and an image sensor for sensing the light source signal from the lens and converting the light source signal into digitized RAW image data, i.e., RAW image data. RAW is in an unprocessed, also uncompressed, format that can be visually referred to as "digital negative". The camera module 1001 may include one camera or two or more cameras.
The memory 1002 may be used for storing applications and data. The memory 1002 stores applications containing executable code. The application programs may constitute various functional modules. The processor 1003 executes various functional applications and data processing by running an application program stored in the memory 1002.
The processor 1003 is a control center of the electronic device, connects various parts of the whole electronic device by using various interfaces and lines, executes various functions of the electronic device and processes data by running or executing an application program stored in the memory 1002 and calling data stored in the memory 1002, thereby performing overall monitoring of the electronic device.
In this embodiment, the processor 1003 in the electronic device loads the executable code corresponding to the processes of one or more application programs into the memory 1002 according to the following instructions, and the processor 1003 runs the application programs stored in the memory 1002, so as to execute:
carrying out brightness enhancement processing before exposure and acquiring basic image data after exposure, or carrying out brightness enhancement processing on the image data obtained after exposure to obtain basic image data;
carrying out noise reduction processing on the basic image data to obtain noise reduction image data;
and carrying out contrast-limited adaptive histogram equalization processing on the noise-reduced image data to obtain a target image.
Referring to fig. 11, the electronic device 1000 may include a camera module 1001, a memory 1002, a processor 1003, a touch display screen 1004, a speaker 1005, a microphone 1006, and the like.
The camera module 1001 may include Image Processing circuitry, which may be implemented using hardware and/or software components, and may include various Processing units that define an Image Signal Processing (Image Signal Processing) pipeline. The image processing circuit may include at least: a camera, an Image Signal Processor (ISP Processor), control logic, an Image memory, and a display. Wherein the camera may comprise at least one or more lenses and an image sensor. The image sensor may include an array of color filters (e.g., Bayer filters). The image sensor may acquire light intensity and wavelength information captured with each imaging pixel of the image sensor and provide a set of raw image data that may be processed by an image signal processor.
The image signal processor may process the raw image data pixel by pixel in a variety of formats. For example, each image pixel may have a bit depth of 8, 10, 12, or 14 bits, and the image signal processor may perform one or more image processing operations on the raw image data, gathering statistical information about the image data. Wherein the image processing operations may be performed with the same or different bit depth precision. The raw image data can be stored in an image memory after being processed by an image signal processor. The image signal processor may also receive image data from an image memory.
The image Memory may be part of a Memory device, a storage device, or a separate dedicated Memory within the electronic device, and may include a DMA (Direct Memory Access) feature.
When image data is received from the image memory, the image signal processor may perform one or more image processing operations, such as temporal filtering. The processed image data may be sent to an image memory for additional processing before being displayed. The image signal processor may also receive processed data from the image memory and perform image data processing on the processed data in the raw domain and in the RGB and YCbCr color spaces. The processed image data may be output to a display for viewing by a user and/or further processed by a Graphics Processing Unit (GPU). Further, the output of the image signal processor may also be sent to an image memory, and the display may read image data from the image memory. In one embodiment, the image memory may be configured to implement one or more frame buffers.
The statistical data determined by the image signal processor may be sent to the control logic. For example, the statistical data may include statistical information of the image sensor such as auto exposure, auto white balance, auto focus, flicker detection, black level compensation, lens shading correction, and the like.
The control logic may include a processor and/or microcontroller that executes one or more routines (e.g., firmware). One or more routines may determine camera control parameters and ISP control parameters based on the received statistics. For example, the control parameters of the camera may include camera flash control parameters, control parameters of the lens (e.g., focal length for focusing or zooming), or a combination of these parameters. The ISP control parameters may include gain levels and color correction matrices for automatic white balance and color adjustment (e.g., during RGB processing), etc.
Referring to fig. 12, fig. 12 is a schematic structural diagram of the image processing circuit in the present embodiment. As shown in fig. 12, for convenience of explanation, only aspects of the image processing technique related to the embodiment of the present invention are shown.
For example, the image processing circuitry may include: camera, image signal processor, control logic ware, image memory, display. The camera may include one or more lenses and an image sensor, among others.
And the first image collected by the camera is transmitted to an image signal processor for processing. After the image signal processor processes the first image, statistical data of the first image (e.g., brightness of the image, contrast value of the image, color of the image, etc.) may be sent to the control logic. The control logic device can determine the control parameters of the camera according to the statistical data, so that the camera can carry out operations such as automatic focusing and automatic exposure according to the control parameters. The first image can be stored in the image memory after being processed by the image signal processor. The image signal processor may also read the image stored in the image memory for processing. In addition, the first image can be directly sent to the display for displaying after being processed by the image signal processor. The display may also read the image in the image memory for display.
In addition, not shown in the figure, the electronic device may further include a CPU and a power supply module. The CPU is connected with the logic controller, the image signal processor, the image memory and the display, and is used for realizing global control. The power supply module is used for supplying power to each module.
The memory 1002 may be used for storing applications and data. The memory 1002 stores applications containing executable code. The application programs may constitute various functional modules. The processor 1003 executes various functional applications and data processing by running an application program stored in the memory 1002.
The processor 1003 is a control center of the electronic device, connects various parts of the whole electronic device by using various interfaces and lines, executes various functions of the electronic device and processes data by running or executing an application program stored in the memory 1002 and calling data stored in the memory 1002, thereby performing overall monitoring of the electronic device.
The touch display screen 1004 may be used to receive a touch input operation by a user and display information such as characters and images.
The microphone 1006 may be used to pick up sound signals in the surrounding environment. For example, the user may emit a voice instructing the electronic device to take an image. The microphone 1006 of the electronic device can pick up the voice, and the processor 1003 of the electronic device 1000 converts the voice into a corresponding voice instruction, and controls the camera module 1001 of the electronic device 1000 to perform an image capturing operation.
In this embodiment, the processor 1003 in the electronic device loads the executable code corresponding to the processes of one or more application programs into the memory 1002 according to the following instructions, and the processor 1003 runs the application programs stored in the memory 1002, so as to execute:
carrying out brightness enhancement processing before exposure and acquiring basic image data after exposure, or carrying out brightness enhancement processing on the image data obtained after exposure to obtain basic image data;
carrying out noise reduction processing on the basic image data to obtain noise reduction image data;
and carrying out contrast-limited adaptive histogram equalization processing on the noise-reduced image data to obtain a target image.
In one embodiment, when the processor 1003 executes the brightness boosting processing before the exposure and acquires the base image data after the exposure, it may execute: the sensitivity is gained before exposure to perform brightness enhancement processing, and basic image data after exposure is acquired.
In one embodiment, when the processor 1003 performs noise reduction processing on the base image data to obtain noise-reduced image data, the following steps may be performed: and performing noise reduction processing on the basic image data by adopting a Gaussian filtering or bilateral filtering mode to obtain noise reduction image data.
In one embodiment, the processor 1003 may further perform: acquiring an ambient light brightness value; if the ambient light brightness value is detected to be smaller than the preset brightness threshold value, brightness improvement processing is carried out before exposure, and exposed basic image data are obtained, or brightness improvement processing is carried out on the exposed image data to obtain the basic image data.
In one embodiment, when the processor 1003 executes the gain sensitivity before exposure for brightness enhancement processing and acquires the base image data after exposure, it may execute: acquiring first sensitivity before exposure, wherein the first sensitivity is the sensitivity used for exposure calculated by the electronic equipment according to a shooting environment; obtaining a gain coefficient; the first sensitivity is gained according to the gain coefficient to obtain a second sensitivity, and the second sensitivity is greater than the first sensitivity; and carrying out exposure according to the second sensitivity, and acquiring basic image data after exposure.
In one embodiment, when the processor 1003 executes the gain sensitivity before exposure for brightness enhancement processing and acquires the base image data after exposure, it may execute: acquiring a third sensitivity before exposure, wherein the third sensitivity is a preset sensitivity numerical value; and replacing the first sensitivity with the third sensitivity to carry out exposure, and acquiring basic image data after exposure, wherein the first sensitivity is calculated by the electronic equipment according to a shooting environment and is used for exposure, and the third sensitivity is greater than the first sensitivity.
In one embodiment, when the processor 1003 executes the obtaining of the gain factor, it may execute: and acquiring an environment light brightness value, and acquiring a gain coefficient according to the environment light brightness value.
In one embodiment, the processor 1003 may further perform: acquiring at least two frames of images, wherein the at least two frames of images are images obtained by processing through the image processing method; and carrying out image registration based on feature point matching on the at least two frames of images.
In the above embodiments, the descriptions of the embodiments have respective emphasis, and parts that are not described in detail in a certain embodiment may refer to the above detailed description of the image processing method, and are not described herein again.
The image processing apparatus provided in the embodiment of the present application and the image processing method in the above embodiment belong to the same concept, and any method provided in the embodiment of the image processing method may be run on the image processing apparatus, and a specific implementation process thereof is described in the embodiment of the image processing method in detail, and is not described herein again.
It should be noted that, for the image processing method described in the embodiment of the present application, it can be understood by those skilled in the art that all or part of the process of implementing the image processing method described in the embodiment of the present application can be completed by controlling the relevant hardware through a computer program, where the computer program can be stored in a computer-readable storage medium, such as a memory, and executed by at least one processor, and during the execution, the process of the embodiment of the image processing method can be included. The storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like.
In the image processing apparatus according to the embodiment of the present application, each functional module may be integrated into one processing chip, each module may exist alone physically, or two or more modules may be integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium, such as a read-only memory, a magnetic or optical disk, or the like.
The foregoing detailed description has provided an image processing method, an image processing apparatus, a storage medium, and an electronic device according to embodiments of the present application, and specific examples are applied herein to explain the principles and implementations of the present application, and the descriptions of the foregoing embodiments are only used to help understand the method and the core ideas of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (9)
1. An image processing method, comprising:
acquiring an ambient light brightness value;
if the ambient light brightness value is detected to be smaller than a preset brightness threshold, acquiring first sensitivity before exposure, performing gain processing on the first sensitivity through a gain coefficient or a preset sensitivity numerical value, and acquiring basic image data after exposure according to the sensitivity after the gain processing, wherein the first sensitivity is the sensitivity which is calculated by the electronic equipment according to a shooting environment and is used for exposure;
carrying out noise reduction processing on the basic image data to obtain noise reduction image data;
and carrying out contrast-limited adaptive histogram equalization processing on the noise-reduced image data to obtain a target image.
2. The image processing method according to claim 1, wherein performing noise reduction processing on the base image data to obtain noise-reduced image data includes:
and performing noise reduction processing on the basic image data by adopting a Gaussian filtering or bilateral filtering mode to obtain noise reduction image data.
3. The image processing method according to claim 1, wherein the acquiring a first sensitivity before the exposure, the gain processing being performed on the first sensitivity by a gain coefficient, and the acquiring the base image data after the exposure at the sensitivity after the gain processing, comprises:
acquiring a first sensitivity before exposure;
obtaining a gain coefficient;
the first sensitivity is gained according to the gain coefficient to obtain a second sensitivity, and the second sensitivity is greater than the first sensitivity;
and carrying out exposure according to the second sensitivity, and acquiring basic image data after exposure.
4. The image processing method according to claim 1, wherein the acquiring a first sensitivity before the exposure, the gain processing being performed on the first sensitivity by a preset sensitivity value, and the acquiring the base image data after the exposure according to the sensitivity after the gain processing comprises:
acquiring a third sensitivity before exposure, wherein the third sensitivity is a preset sensitivity numerical value;
and replacing the first sensitivity with the third sensitivity, and acquiring basic image data after exposure, wherein the third sensitivity is greater than the first sensitivity.
5. The image processing method according to claim 3, wherein the obtaining the gain factor comprises:
and acquiring an environment light brightness value, and acquiring a gain coefficient according to the environment light brightness value.
6. The image processing method according to claim 1, characterized in that the image processing method further comprises:
acquiring at least two frames of images, wherein the at least two frames of images are images obtained by processing through the image processing method;
and carrying out image registration based on feature point matching on the at least two frames of images.
7. An image processing apparatus characterized by comprising:
the first processing module is used for acquiring an ambient light brightness value; if the ambient light brightness value is detected to be smaller than a preset brightness threshold, acquiring first sensitivity before exposure, performing gain processing on the first sensitivity through a gain coefficient or a preset sensitivity numerical value, and acquiring basic image data after exposure according to the sensitivity after the gain processing, wherein the first sensitivity is the sensitivity which is calculated by the electronic equipment according to a shooting environment and is used for exposure;
the noise reduction module is used for carrying out noise reduction processing on the basic image data to obtain noise reduction image data;
and the second processing module is used for carrying out contrast-limited adaptive histogram equalization processing on the noise-reduced image data to obtain a target image.
8. A storage medium having stored thereon a computer program, characterized in that the computer program, when executed on a computer, causes the computer to execute the method according to any of claims 1 to 6.
9. An electronic device comprising a memory, a processor, wherein the processor is configured to perform the method of any of claims 1 to 6 by invoking a computer program stored in the memory.
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| CN109101950A (en) * | 2018-08-31 | 2018-12-28 | 福州依影健康科技有限公司 | A kind of optic disk localization method and storage equipment based on the fitting of main blood vessel |
| CN109711322A (en) * | 2018-12-24 | 2019-05-03 | 天津天地伟业信息系统集成有限公司 | A kind of people's vehicle separation method based on RFCN |
| CN110207671A (en) * | 2018-12-29 | 2019-09-06 | 中国科学院软件研究所 | A kind of space-based intelligence imaging system |
| CN110059694A (en) * | 2019-04-19 | 2019-07-26 | 山东大学 | The intelligent identification Method of lteral data under power industry complex scene |
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| CN110519485A (en) | 2019-11-29 |
| WO2021047408A1 (en) | 2021-03-18 |
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