CN111738949B - Image brightness adjusting method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application discloses an image brightness adjusting method, an image brightness adjusting device, electronic equipment and a storage medium, relates to the fields of artificial intelligence, deep learning and image processing, and can be particularly applied to the aspect of fundus image screening. The specific scheme is as follows: obtaining an observation image; wherein the observed image is an image of red, green and blue color space; separating a background image corresponding to the observation image from the observation image; determining a display parameter value corresponding to the observation image according to the background image; and adjusting the brightness of the observation image according to the display parameter value corresponding to the observation image. According to the embodiment of the application, the proper display parameter value can be intelligently selected according to the brightness distribution of the observed image, so that the brightness distribution of the observed image is more reasonable.
Description
Technical Field
The application relates to the technical field of computers, and further relates to the fields of artificial intelligence, deep learning and image processing, in particular to an image adjustment method, an image adjustment device, electronic equipment and a storage medium.
Background
The fundus image adaptive brightness adjustment technique (Fundus image adaptive brightness adjustment) refers to automatic adjustment according to the self brightness distribution of the fundus image, so that the image quality meets the unified specification. In an actual service scene, due to factors such as model difference, insufficient illumination, too small dynamic range of an imaging sensor, photographing level of a technician and the like of an image acquisition device, brightness distribution of a fundus image obtained by the image acquisition device is uneven, quality is poor, and a gap exists between a fundus image actually entering an algorithm model and a fundus image required by the model in an initial training stage, so that performance of the whole system can be influenced. Therefore, the acquired fundus image needs to be preprocessed before the fundus image is input to the algorithm model, so as to reduce the influence of the image acquisition apparatus on the original fundus image.
In the prior art, there is a great deal of focus on enhancing retinal blood vessels to achieve better vessel segmentation by increasing the contrast between the vessels and the retinal background in gray scale and color retinal images. The contrast-based enhancement method mainly comprises three modes of histogram-based, filter-based and transformation-based; for example, histogram matching between red and green channels is used as a preprocessing step for vessel segmentation, which can improve contrast of overall dark features such as vessels, but can reduce contrast of bright objects with tiny dark objects such as Microangiomas (MA); enhancement with matched filters can improve local contrast and help with vessel segmentation, but does not preserve image fidelity, which can also affect other structures present in the image; the enhancement mode based on the contourlet transformation is poor in the region of poor contrast of the fundus image. Further, the luminance-based enhancement method includes: neighborhood-based color retinal image enhancement (Colour Retinal Image Enhancement based on Domain Knowledge) and brightness and contrast adjustment-based color retinal image enhancement (Color Retinal Image Enhancement Based on Luminosity and Contrast Adjustment). In the brightness-based enhancement method, brightness adjustment is performed on different observation images using fixed display parameter values, and an appropriate display parameter value cannot be intelligently selected according to brightness distribution of the observation images themselves.
Disclosure of Invention
The application provides a method, a device, equipment and a storage medium for adjusting image brightness, which can intelligently select proper display parameter values according to the brightness distribution of an observation image, so that the brightness distribution of the observation image is more reasonable.
In a first aspect, the present application provides a method for adjusting brightness of an image, the method including:
obtaining an observation image; wherein the observed image is an image of red, green and blue color space;
separating a background image corresponding to the observed image from the observed image;
determining a display parameter value corresponding to the observation image according to the background image;
and adjusting the brightness of the observation image according to the display parameter value corresponding to the observation image.
In a second aspect, the present application provides an apparatus for adjusting brightness of an image, the apparatus comprising: the device comprises an acquisition module, a separation module, a determination module and an adjustment module; wherein,
the acquisition module is used for acquiring an observation image; wherein the observed image is an image of red, green and blue color space;
the separation module is used for separating a background image corresponding to the observation image from the observation image;
The determining module is used for determining a display parameter value corresponding to the observation image according to the background image;
the adjusting module is used for adjusting the brightness of the observation image according to the display parameter value corresponding to the observation image.
In a third aspect, an embodiment of the present application provides an electronic device, including:
one or more processors;
a memory for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method for adjusting image brightness according to any embodiment of the present application.
In a fourth aspect, embodiments of the present application provide a storage medium having stored thereon a computer program that, when executed by a processor, implements the method for adjusting brightness of an image according to any embodiment of the present application.
According to the technical scheme provided by the application, the technical problem that the fixed display parameter values are used for adjusting the brightness of different observation images in the prior art, and the proper display parameter values cannot be intelligently selected according to the brightness distribution of the observation images is solved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for better understanding of the present solution and do not constitute a limitation of the present application. Wherein:
fig. 1 is a flowchart of a method for adjusting brightness of an image according to an embodiment of the present disclosure;
fig. 2 (a) is a schematic diagram of the positions of sampling points in a fundus image according to the first embodiment of the present application;
fig. 2 (b) is a schematic diagram showing the distribution of sampling points in a fundus image according to the first embodiment of the present application;
fig. 3 is a flowchart of a method for adjusting image brightness according to a second embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a background diagram provided in the second embodiment of the present application;
fig. 5 is a flowchart of a method for adjusting image brightness according to the third embodiment of the present application;
fig. 6 (a) is a schematic structural diagram of a luminance drift factor according to a third embodiment of the present application;
FIG. 6 (b) is a schematic structural diagram of a contrast drift factor according to a third embodiment of the present application;
FIG. 7 is a schematic diagram of a convolution kernel provided in embodiment III of the present application;
Fig. 8 is a schematic diagram of comparison between the image brightness adjustment provided in the third embodiment of the present application;
fig. 9 is a schematic structural diagram of an image brightness adjusting device according to a fourth embodiment of the present disclosure;
fig. 10 is a schematic structural view of a separation module according to a fourth embodiment of the present application;
fig. 11 is a schematic structural diagram of a determining module provided in the fourth embodiment of the present application;
fig. 12 is a schematic structural diagram of an adjustment module according to a fourth embodiment of the present disclosure;
fig. 13 is a block diagram of an electronic device for implementing the method for adjusting image brightness according to the embodiment of the present application.
Detailed Description
Exemplary embodiments of the present application are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present application to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Example 1
Fig. 1 is a flowchart of a method for adjusting image brightness according to an embodiment of the present application, where the method may be performed by an apparatus for adjusting image brightness or an electronic device, and the apparatus or the electronic device may be implemented by software and/or hardware, and the apparatus or the electronic device may be integrated in any intelligent device having a network communication function. As shown in fig. 1, the method for adjusting the brightness of an image may include the steps of:
S101, obtaining an observation image; the observation image is an image in red, green and blue color space.
In a specific embodiment of the present application, an electronic device may acquire an observation image; the observation image is an image in red, green and blue color space (i.e., RGB space). The RGB space is based on three basic colors of Red (Red), green (Green) and Blue (Blue), and is superimposed to different degrees to generate rich and wide colors, so the RGB space is commonly called a three-primary-color mode. RGB space is the most commonly used model in life, and most of televisions, computer monitors and the like use the model. Any color in the nature can be formed by mixing red, green and blue light, and most of colors seen by people in real life are mixed colors.
S102, separating a background image corresponding to the observation image from the observation image.
In a specific embodiment of the present application, the electronic device may separate a background image corresponding to the observed image from the observed image. Specifically, the electronic device may first separate a green channel (i.e., G channel) image corresponding to the observation image from the observation image; then calculating the mean value and standard deviation of each pixel point in the G channel image; wherein, the pixel point in the G channel image comprises: sampling points and non-sampling points; and separating a background image corresponding to the G channel image from the G channel image according to the mean value and standard deviation of each pixel point in the G channel image and the pixel value of each pixel point.
Fig. 2 (a) is a schematic diagram of the positions of sampling points in a fundus image according to the first embodiment of the present application. As shown in fig. 2 (a), according to the characteristics that the illumination of the central area is better and the illumination of the peripheral area is relatively worse, fewer points can be collected in the central area as sampling points, and more points can be collected in the peripheral area as sampling points. The units of the abscissa and the ordinate in fig. 2 (a) are the basic units of pixel points.
Fig. 2 (b) is a schematic diagram of the distribution of sampling points in the fundus image provided in the first embodiment of the present application. As shown in fig. 2 (b), a polar coordinate system is established with the center point of the fundus image as the origin, and sampling points of the fundus image are distributed on five circumferences with the origin of the polar coordinates as the center of a circle; because the position of each sampling point is fixed, the angle and the radius of each sampling point relative to the origin point can be known in advance, so that the polar coordinates of each sampling point can be obtained, and then the rectangular coordinates of each sampling point can be calculated according to the conversion relation between the polar coordinates and the rectangular coordinates.
S103, determining display parameter values corresponding to the observation images according to the background images.
In a specific embodiment of the present application, the electronic device may determine, according to the background image, a display parameter value corresponding to the observed image; the display parameter value is gamma value. Specifically, the electronic device may calculate, according to a predetermined window size and a pixel value of each pixel in the background image, a luminance drift factor corresponding to each pixel in the background image; and then determining a gamma value corresponding to the observed image according to the brightness drift factors corresponding to the pixel points in the background image.
S104, adjusting the brightness of the observation image according to the display parameter value corresponding to the observation image.
In a specific embodiment of the present application, the electronic device may adjust the brightness of the observed image according to the display parameter value corresponding to the observed image. Specifically, the electronic device may calculate a brightness gain matrix of the observed image according to the gamma value corresponding to the observed image; and then adjusting the brightness of the observed image according to the brightness gain matrix.
The method for adjusting the brightness of the image provided by the embodiment of the application comprises the steps of firstly obtaining an observation image; then separating a background image corresponding to the observation image from the observation image; determining a display parameter value corresponding to the observation image according to the background image; and finally, adjusting the brightness of the observation image according to the display parameter value corresponding to the observation image. That is, the present application can determine the display parameter value corresponding to the observation image in the background image corresponding to the observation image, so that the brightness of the observation image can be adjusted according to the display parameter value corresponding to the observation image. In the conventional image brightness adjustment method, the brightness of different observation images is adjusted by using fixed display parameter values, and the appropriate display parameter values cannot be intelligently selected according to the brightness distribution of the observation images. Because the technical means of separating the background image corresponding to the observation image from the observation image and determining the display parameter value corresponding to the observation image according to the background image are adopted, the technical problems that in the prior art, fixed display parameter values are used for adjusting the brightness of different observation images, and proper display parameter values cannot be intelligently selected according to the brightness distribution of the observation image are overcome, and the proper display parameter values can be intelligently selected according to the brightness distribution of the observation image are solved, so that the brightness distribution of the observation image is more reasonable; in addition, the technical scheme of the embodiment of the application is simple and convenient to realize, convenient to popularize and wider in application range.
Example two
Fig. 3 is a flowchart illustrating a method for adjusting image brightness according to a second embodiment of the present disclosure. As shown in fig. 3, the method for adjusting the brightness of an image may include the steps of:
s301, obtaining an observation image; the observation image is an image in red, green and blue color space.
S302, separating a green channel image corresponding to the observation image from the observation image.
In a specific embodiment of the present application, the electronic device may separate a G-channel image corresponding to the observation image from the observation image. Since the G-channel image retains a large amount of contrast information, a background image extraction operation is performed on the G-channel, and pixel points of the background image are extracted from the observation image using the mean and standard deviation of the sampling points.
S303, calculating the mean value and standard deviation of each pixel point in the green channel image; wherein, the pixel in the green channel image includes: sampling points and non-sampling points.
In a specific embodiment of the present application, the electronic device may calculate the mean and standard deviation of each pixel point in the G-channel image; wherein, the pixel point in the G channel image comprises: sampling points and non-sampling points. Specifically, the electronic device may calculate, according to a predetermined window size corresponding to each sampling point, a mean value and a standard deviation of each sampling point in the G channel image; and then calculating the mean value and the standard deviation of each non-sampling point in the G channel according to the mean value and the standard deviation of each sampling point in the G channel. Assume that the five circumferences shown in FIG. 2 (b) are each spaced from the center of the circle by a distance d 1 、d 2 、d 3 、d 4 、d 5 The window sizes corresponding to the sampling points on the five circumferences are w respectively 1 、w 2 、w 3 、w 4 、w 5 . Therefore, the electronic equipment can calculate the average value and standard deviation of each sampling point in the G channel image according to the window size corresponding to each sampling point; then at samplingAnd (3) carrying out double-line interpolation calculation between the points to obtain a mean mu (x, y) and a variance sigma (x, y) of all the pixel points. Since the distances between different sampling points and the circle center are different, the window sizes adopted by different pixel points are also different. In the practical process, the window size is not limited to d 1 -d 5 These five values.
S304, separating a background image corresponding to the green channel image from the green channel image according to the mean value and standard deviation of each pixel point in the green channel image and the pixel value of each pixel point.
In a specific embodiment of the present application, the electronic device may separate a background image corresponding to the G channel image from the G channel image according to a mean value and a standard deviation of each pixel point in the G channel image and a pixel value of each pixel point. Fig. 4 is a schematic structural diagram of a background diagram provided in the second embodiment of the present application. As shown in fig. 4, the electronic device may extract a pixel point from the pixel points in the G channel image as a current pixel point, and then calculate a mahalanobis distance corresponding to the current pixel point according to the mean value and standard deviation of the current pixel point and the pixel value of the current pixel point; if the mahalanobis distance corresponding to the current pixel point is smaller than or equal to the preset threshold value, the electronic device can use the current pixel point as one pixel point in the background image corresponding to the G channel image; if the mahalanobis distance corresponding to the current pixel point is greater than the preset threshold value, the electronic equipment can take the current pixel point as one pixel point in the foreground image corresponding to the G channel image; and repeatedly executing the operation until each pixel point in the G channel image is determined as the pixel point in the background image or the pixel point in the foreground image. Specifically, the electronic device may calculate the mahalanobis distance corresponding to the current pixel point according to the following formula: Wherein D (x, y) represents the Marshall distance corresponding to the current pixel point; g (x, y) represents the pixel value of the current pixel point; μ (x, y) represents the mean value of the current pixel point; σ (x, y) represents the standard deviation of the current pixel point.
S305, determining display parameter values corresponding to the observation images according to the background images.
In a specific embodiment of the present application, the electronic device may determine, according to the background map, a display parameter value corresponding to the observed image. Specifically, the electronic device may calculate, according to a predetermined window size and a pixel value of each pixel in the background image, a luminance drift factor corresponding to each pixel in the background image; and then determining a gamma value corresponding to the observed image according to the brightness drift factors corresponding to the pixel points in the background image.
S306, adjusting the brightness of the observation image according to the display parameter value corresponding to the observation image.
The method for adjusting the brightness of the image provided by the embodiment of the application comprises the steps of firstly obtaining an observation image; then separating a background image corresponding to the observation image from the observation image; determining a display parameter value corresponding to the observation image according to the background image; and finally, adjusting the brightness of the observation image according to the display parameter value corresponding to the observation image. That is, the present application can determine the display parameter value corresponding to the observation image in the background image corresponding to the observation image, so that the brightness of the observation image can be adjusted according to the display parameter value corresponding to the observation image. In the conventional image brightness adjustment method, the brightness of different observation images is adjusted by using fixed display parameter values, and the appropriate display parameter values cannot be intelligently selected according to the brightness distribution of the observation images. Because the technical means of separating the background image corresponding to the observation image from the observation image and determining the display parameter value corresponding to the observation image according to the background image are adopted, the technical problems that in the prior art, fixed display parameter values are used for adjusting the brightness of different observation images, and proper display parameter values cannot be intelligently selected according to the brightness distribution of the observation image are overcome, and the proper display parameter values can be intelligently selected according to the brightness distribution of the observation image are solved, so that the brightness distribution of the observation image is more reasonable; in addition, the technical scheme of the embodiment of the application is simple and convenient to realize, convenient to popularize and wider in application range.
Example III
Fig. 5 is a flowchart illustrating a method for adjusting image brightness according to the third embodiment of the present application. As shown in fig. 5, the method for adjusting the brightness of an image may include the steps of:
s501, obtaining an observation image; the observation image is an image in red, green and blue color space.
S502, separating a green channel image corresponding to the observation image from the observation image.
S503, calculating the mean value and standard deviation of each pixel point in the green channel image; wherein, the pixel in the green channel image includes: sampling points and non-sampling points.
S504, separating a background image corresponding to the green channel image from the green channel image according to the mean value and standard deviation of each pixel point in the green channel image and the pixel value of each pixel point.
In the process of image acquisition, the brightness and contrast of the image can generate deformation on the original image, and finally the image observed by human eyes is obtained, wherein the deformation can be described by the following observation model: i (x, y) =c (x, y) ×i 0 (x, y) +l (x, y); wherein I (x, y) represents a pixel value of each pixel point in the observation image; i 0 (x, y) represents a pixel value of each pixel point in the original image; c (x, y) represents a contrast drift factor corresponding to each pixel point in the original image; l (x, y) represents a luminance drift factor corresponding to each pixel point in the original image. Further, the original image refers to an ideal fundus image that does not contain the effects of non-uniform brightness and contrast, and the original image can be seen as a superimposed combination of ideal background and foreground images: Wherein (1)>A pixel value representing each pixel point in the foreground image;representing the pixel value of each pixel point in the background image. In the foreground image of the original eye bottom map, -a new eye bottom map>Is the fundus omentum area prospect, comprising vascular structures, optic discs, optic cups and any visible lesions; />Is the background of the fundus omentum area, and does not contain vascular structures, optic discs, optic cups and any visible lesions. The formula of the original image can be obtained according to the observation model description: />It can be seen that if the true brightness drift factor L (x, y) and contrast drift factor C (x, y) corresponding to each pixel point in the original image can be determined, the pixel value I of each pixel point in the original image can be obtained according to the above formula 0 (x, y). The true brightness drift factor L (x, y) and contrast drift factor C (x, y) for each pixel in the original image are typically unknown and can only be estimated from the pixel value I (x, y) for each pixel in the observed image. Therefore, the above formula of the original image can be changed into the following form:wherein I is 0’ (x, y) is an estimate of the pixel value of each pixel point in the original image; l' (x, y) is an estimate of the luminance drift factor corresponding to each pixel in the original image; c' (x, y) is an estimate of the contrast drift factor corresponding to each pixel point in the original image. To obtain I 0’ (x, y) L '(x, y) and C' (x, y) must be estimated. The foreground images have large characteristic differences, the background images have smooth changes, and normal distribution can be used for modeling:wherein mu b Ideal for background picturesUniform brightness value; sigma (sigma) b The nature of the background map in the spatial domain is reflected.
S505, calculating brightness drift factors corresponding to all pixel points in the background image according to the predetermined window size and the pixel values of all pixel points in the background image.
In a specific embodiment of the present application, the electronic device may be configured to determine the window size w according to a predetermined window size 0 And calculating the average value and standard deviation of each pixel in the background image, wherein the average value of each pixel is the brightness drift factor L' (x, y) corresponding to each pixel; the standard deviation of each pixel point is the contrast drift factor C' (x, y) corresponding to each pixel point.
Fig. 6 (a) is a schematic structural diagram of a luminance drift factor according to a third embodiment of the present application; fig. 6 (b) is a schematic structural diagram of a contrast drift factor according to a third embodiment of the present application. As shown in fig. 6 (a) and 6 (b), w 0 E (15, 25), calculate window w 0 ×w 0 The average value and standard deviation of each pixel point (x, y) are the brightness drift factors corresponding to the pixel points; the standard deviation is the contrast drift factor corresponding to the pixel point. The units of the abscissa and the ordinate in fig. 6 (a) and 6 (b) are the basic units of pixel points.
In the specific embodiment of the application, when the electronic equipment calculates the mean value and standard deviation of each sampling point, the traditional double-layer circulation traversing mode is serious in time consumption, the extraction of one background image needs 1.1 seconds, and the application adopts the convolution multiplication mode to replace the circulation of image pixels, so that the code efficiency is greatly improved.
Fig. 7 is a schematic structural diagram of a convolution kernel provided in the third embodiment of the present application. As shown in fig. 7, a convolution kernel k of (w, w) is set, and L' (x, y) can be obtained by convolution multiplication of k and G channel images G (x, y): l' (x, y) =k×g (x, y); for C' (x, y):
then->Wherein i represents the order of each pixel point in the G channel image; n represents the number of pixel points in the G channel image; x is x i A pixel value representing an i-th pixel point; mu is the arithmetic average of the ith pixel to the nth pixel. The background image extraction process is reduced from 1.1 seconds to 0.06 seconds, the speed is increased by 18.3 times, and the whole self-adaptive brightness adjustment whole process only needs 0.2 seconds, so that real-time amplification is supported in the training process.
S506, according to brightness drift factors corresponding to all pixel points in the background image, display parameter values corresponding to the observed image are determined.
In a specific embodiment of the present application, the electronic device may determine a display parameter value corresponding to the observed image according to a luminance drift factor corresponding to each pixel point in the background image. Specifically, the electronic device may extract the luminance drift factor L' (x, y) corresponding to each pixel, sort the pixels in order from small to large, and then obtain the value of the p1 th percentile and the value of the p2 nd percentile in the sorting result, where the values are respectively recorded as: img_permanent_p1 and img_permanent_p2; wherein p1 < p2; and determining a gamma value corresponding to the brightness distribution of the observed image I (x, y) according to the value ranges of img_peripheral_p1 and img_peripheral_p2. Preferably, the value range of p1 is: 70-100 parts; the value range of p2 is as follows: 10 to 40 percent. Further, if the value corresponding to the second percentile is smaller than the first value, the electronic device may determine the gamma value corresponding to the observed image as one gamma value in the first gamma value set; if the value corresponding to the first percentile is greater than the second value, the electronic device may determine the gamma value corresponding to the observed image as one gamma value in the second gamma value set; if the value corresponding to the first percentile is smaller than the third value and the value corresponding to the second percentile is larger than the fourth value, the electronic device may determine the gamma value corresponding to the observed image as one gamma value in the third gamma value set; wherein the first value is less than the fourth value; the fourth value is less than the third value; the third value is less than the second value. For example, if img_periodic_p2 is lower than the first threshold th1 (th 1 has a value ranging from 15 to 45), the gamma value takes g1 (g 1 has a value ranging from 1.9 to 2.2); if img_periodic_p1 is higher than a second threshold value th2 (the value range of th2 is 170-200), the gamma value takes g2 (the value range of g2 is 0.5-0.8); if img_periodic_p1 is lower than the third threshold th3 (the value range of th3 is 140-160) and img_periodic_p2 is higher than the fourth threshold th4 (the value range of th4 is 70-90), the gamma value takes g3 (the value range of g3 is 0.9-1.1); in the rest cases, the gamma value is g4 (the value range of g4 is 1.5-1.8).
S507, adjusting the brightness of the observation image according to the display parameter value corresponding to the observation image.
In a specific embodiment of the present application, the electronic device may adjust the brightness of the observed image according to the display parameter value corresponding to the observed image. Specifically, the electronic device may calculate a brightness gain matrix of the observed image according to the gamma value corresponding to the observed image; and then adjusting the brightness of the observed image according to the brightness gain matrix. Since the RGB channels contain both luminance information and color information, they are interrelated. In order to obtain a brightness gain matrix irrelevant to colors, an observation image can be firstly converted into an HSV space from an RGB space; then, brightness of each pixel point in the V-channel image corresponding to the observed image is adjusted by using a gamma value corresponding to the observed image; and determining a brightness gain matrix G (x, y) =V '/V of the observed image according to the brightness value V' of each pixel point in the V-channel image after adjustment and the brightness value V before adjustment. To enhance brightness while ensuring that the color information is unchanged, the three R, G, B channels should be adjusted in the same ratio:wherein R' (x, y) is the adjusted brightness value of each pixel point in the R channel image; r (x, y) is the brightness value of each pixel point in the R channel image before adjustment; g' (x, y) is the brightness of each pixel point in the G channel image after adjustment A degree value; g (x, y) is the brightness value of each pixel point in the G channel image before adjustment; b' (x, y) is the adjusted brightness value of each pixel point in the B channel image; b (x, y) is the brightness value of each pixel point in the B channel image before adjustment. Therefore, the G (x, y) is applied to the RGB channels respectively, and a picture with optimized brightness is obtained. If gamma value correction is performed directly in the RGB space, the color information is also changed. The brightness gain matrix is corrected based on gamma value, and is a more reasonable method for improving brightness. And the selection of the gamma value determines the direction of adjustment of the brightness of the image.
Fig. 8 is a schematic diagram of comparison between the brightness adjustment of the image provided in the third embodiment of the present application. As shown in fig. 8, it can be seen that the original dark place is adjusted to the normal brightness level, while the original bright place, such as the video disc area, is kept unchanged, and the brightness distribution of the adjusted image is uniform. For a normal eye fundus image with uniform brightness distribution, the gamma value is also automatically taken as 1, i.e. the original distribution is not changed. According to the method and the system, the brightness of the image can be adjusted according to the brightness distribution of the image, so that the image is distributed close to the training set, various actual combat scenes can be effectively treated, an automatic screening system can screen diseases more accurately and with high quality, and misdiagnosis caused by low quality is reduced as much as possible; the brightness distribution is adaptively adjusted, the original image color distribution is not changed, and the naturalness of the eye bottom image is reserved; in addition, the method has high time efficiency and supports real-time enhancement in the training process; the existing methods are long in time and cannot support online processing during model training; in addition, the method and the device can perform reverse enhancement, namely, a reverse gamma value is determined according to the brightness distribution of the original image, so that a low-quality image in a real scene is simulated in the training process, and the model is more robust to fundus images with uneven brightness distribution.
The method for adjusting the brightness of the image provided by the embodiment of the application comprises the steps of firstly obtaining an observation image; then separating a background image corresponding to the observation image from the observation image; determining a display parameter value corresponding to the observation image according to the background image; and finally, adjusting the brightness of the observation image according to the display parameter value corresponding to the observation image. That is, the present application can determine the display parameter value corresponding to the observation image in the background image corresponding to the observation image, so that the brightness of the observation image can be adjusted according to the display parameter value corresponding to the observation image. In the conventional image brightness adjustment method, the brightness of different observation images is adjusted by using fixed display parameter values, and the appropriate display parameter values cannot be intelligently selected according to the brightness distribution of the observation images. Because the technical means of separating the background image corresponding to the observation image from the observation image and determining the display parameter value corresponding to the observation image according to the background image are adopted, the technical problems that in the prior art, fixed display parameter values are used for adjusting the brightness of different observation images, and proper display parameter values cannot be intelligently selected according to the brightness distribution of the observation image are overcome, and the proper display parameter values can be intelligently selected according to the brightness distribution of the observation image are solved, so that the brightness distribution of the observation image is more reasonable; in addition, the technical scheme of the embodiment of the application is simple and convenient to realize, convenient to popularize and wider in application range.
Example IV
Fig. 9 is a schematic structural diagram of an image brightness adjusting device according to a fourth embodiment of the present application. As shown in fig. 9, the apparatus 900 includes: an acquisition module 901, a separation module 902, a determination module 903 and an adjustment module 904; wherein,
the acquiring module 901 is configured to acquire an observation image; wherein the observed image is an image of red, green and blue color space;
the separation module 902 is configured to separate a background image corresponding to the observed image from the observed image;
the determining module 903 is configured to determine a display parameter value corresponding to the observed image according to the background image;
the adjusting module 904 is configured to adjust the brightness of the observed image according to the display parameter value corresponding to the observed image.
Fig. 10 is a schematic structural diagram of a separation module according to a fourth embodiment of the present application. As shown in fig. 10, the separation module 902 includes: separating the sub-module 9021 and the first calculation sub-module 9022; wherein,
the separation submodule 9021 is configured to separate a green channel image corresponding to the observed image from the observed image;
the first calculating submodule 9022 is used for calculating the mean value and standard deviation of each pixel point in the green channel image; wherein, the pixel point in the green channel image includes: sampling points and non-sampling points;
The separating submodule 9021 is further configured to separate a background image corresponding to the green channel image from the green channel image according to the mean value and the standard deviation of each pixel point in the green channel image and the pixel value of each pixel point.
Further, the first calculating submodule 9022 is specifically configured to calculate, according to a predetermined window size corresponding to each sampling point, a mean value and a standard deviation of each sampling point in the green channel image; and calculating the mean value and the standard deviation of each non-sampling point in the green channel according to the mean value and the standard deviation of each sampling point in the green channel.
Further, the determining module 903 is further configured to sample the observed image to obtain each sampling point of the observed image; and determining the size of a window corresponding to each sampling point according to the distance between each sampling point and the center point in the observed image.
Further, the separation submodule 9021 is specifically configured to extract a pixel point from the pixel points in the green channel image as a current pixel point, and calculate a mahalanobis distance corresponding to the current pixel point according to the mean value and standard deviation of the current pixel point and the pixel value of the current pixel point; if the mahalanobis distance corresponding to the current pixel point is smaller than or equal to a preset threshold value, the current pixel point is used as one pixel point in the background image corresponding to the green channel image; if the mahalanobis distance corresponding to the current pixel point is larger than the preset threshold value, the current pixel point is used as one pixel point in the foreground image corresponding to the green channel image; and repeatedly executing the operation until each pixel point in the green channel image is determined as the pixel point in the background image or the pixel point in the foreground image.
Fig. 11 is a schematic structural diagram of a determining module provided in the fourth embodiment of the present application. As shown in fig. 11, the determining module 903 includes: a second calculation sub-module 9031 and a determination sub-module 9032; wherein,
the second calculating submodule 9031 is configured to calculate a brightness drift factor corresponding to each pixel point in the background map according to a predetermined window size and a pixel value of each pixel point in the background map;
the determining submodule 9032 is configured to determine a display parameter value corresponding to the observed image according to a brightness drift factor corresponding to each pixel point in the background image.
Further, the determining submodule 9032 is specifically configured to sort the luminance drift factors corresponding to all the pixels according to the luminance drift factors corresponding to the pixels in the background image; determining a value corresponding to the first percentile and a value corresponding to the second percentile in the sorted brightness drift factors; wherein the first percentile is less than the second percentile; and determining the display parameter value corresponding to the observation image according to the value corresponding to the first percentile and/or the distribution interval of the value corresponding to the second percentile.
Further, the determining submodule 9032 is specifically configured to determine, if the value corresponding to the second percentile is smaller than the first value, the display parameter value corresponding to the observed image as one display parameter value in the first display parameter value set; if the value corresponding to the first percentile is larger than the second value, determining the display parameter value corresponding to the observation image as one display parameter value in a second display parameter value set; if the value corresponding to the first percentile is smaller than the third value and the value corresponding to the second percentile is larger than the fourth value, determining the display parameter value corresponding to the observed image as one display parameter value in a third display parameter value set; wherein the first value is less than the fourth value; the fourth value is less than the third value; the third value is less than the second value.
Fig. 12 is a schematic structural diagram of an adjustment module according to a fourth embodiment of the present application. As shown in fig. 12, the adjustment module 904 includes: a third calculation sub-module 9041 and an adjustment sub-module 9042; wherein,
the third calculation sub-module 9041 is configured to calculate a brightness gain matrix of the observed image according to the display parameter value corresponding to the observed image;
The adjusting submodule 9042 is configured to adjust the brightness of the observed image according to the brightness gain matrix.
Further, the third computing sub-module 9041 is specifically configured to convert the observed image from the red, green, and blue color space to a hue saturation brightness space; adjusting the brightness of each pixel point in the brightness channel image corresponding to the observed image by using the display parameter value corresponding to the observed image; and determining a brightness gain matrix of the observed image according to the brightness value of each pixel point in the brightness channel image after adjustment and the brightness value before adjustment.
The image brightness adjusting device can execute the method provided by any embodiment of the application, and has the corresponding functional modules and beneficial effects of the executing method. Technical details not described in detail in this embodiment may be referred to the method for adjusting brightness of an image provided in any embodiment of the present application.
Example five
According to embodiments of the present application, an electronic device and a readable storage medium are also provided.
As shown in fig. 13, a block diagram of an electronic device according to an image brightness adjustment method according to an embodiment of the present application is shown. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the application described and/or claimed herein.
As shown in fig. 13, the electronic device includes: one or more processors 1301, memory 1302, and interfaces for connecting the components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the electronic device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In other embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple electronic devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 1301 is illustrated in fig. 13.
Memory 1302 is a non-transitory computer-readable storage medium provided herein. The memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method for adjusting image brightness provided herein. The non-transitory computer-readable storage medium of the present application stores computer instructions for causing a computer to execute the image brightness adjustment method provided by the present application.
The memory 1302 is used as a non-transitory computer readable storage medium, and may be used to store a non-transitory software program, a non-transitory computer executable program, and modules, such as program instructions/modules (e.g., the acquisition module 901, the separation module 902, the determination module 903, and the adjustment module 904 shown in fig. 9) corresponding to the image brightness adjustment method in the embodiments of the present application. The processor 1301 executes various functional applications of the server and data processing, that is, implements the image brightness adjustment method in the above-described method embodiment, by executing non-transitory software programs, instructions, and modules stored in the memory 1302.
Memory 1302 may include a storage program area that may store an operating system, at least one application program required for functionality, and a storage data area; the storage data area may store data created according to the use of the electronic device of the adjustment method of the image brightness, and the like. In addition, memory 1302 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory 1302 may optionally include memory located remotely from processor 1301, which may be connected to the electronic device of the image brightness adjustment method via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the method for adjusting image brightness may further include: an input device 1303 and an output device 1304. The processor 1301, memory 1302, input device 1303, and output device 1304 may be connected by a bus or other means, for example in fig. 13.
The input device 1303 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic apparatus of the image brightness adjustment method, such as input devices of a touch screen, a keypad, a mouse, a track pad, a touch pad, a joystick, one or more mouse buttons, a track ball, a joystick, and the like. The output device 1304 may include a display device, auxiliary lighting (e.g., LEDs), and haptic feedback (e.g., a vibrating motor), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device may be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASIC (application specific integrated circuit), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
These computing programs (also referred to as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), the internet, and blockchain networks.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
According to the technical scheme of the embodiment of the application, an observation image is acquired first; then separating a background image corresponding to the observation image from the observation image; determining a display parameter value corresponding to the observation image according to the background image; and finally, adjusting the brightness of the observation image according to the display parameter value corresponding to the observation image. That is, the present application can determine the display parameter value corresponding to the observation image in the background image corresponding to the observation image, so that the brightness of the observation image can be adjusted according to the display parameter value corresponding to the observation image. In the conventional image brightness adjustment method, the brightness of different observation images is adjusted by using fixed display parameter values, and the appropriate display parameter values cannot be intelligently selected according to the brightness distribution of the observation images. Because the technical means of separating the background image corresponding to the observation image from the observation image and determining the display parameter value corresponding to the observation image according to the background image are adopted, the technical problems that in the prior art, fixed display parameter values are used for adjusting the brightness of different observation images, and proper display parameter values cannot be intelligently selected according to the brightness distribution of the observation image are overcome, and the proper display parameter values can be intelligently selected according to the brightness distribution of the observation image are solved, so that the brightness distribution of the observation image is more reasonable; in addition, the technical scheme of the embodiment of the application is simple and convenient to realize, convenient to popularize and wider in application range.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially, or in a different order, provided that the desired results of the technical solutions disclosed in the present application can be achieved, and are not limited herein.
The above embodiments do not limit the scope of the application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application are intended to be included within the scope of the present application.
Claims (18)
1. A method for adjusting brightness of an image, the method comprising:
obtaining an observation image; wherein the observed image is an image of red, green and blue color space;
separating a background image corresponding to the observed image from the observed image;
calculating brightness drift factors corresponding to all pixel points in the background image according to the predetermined window size and the pixel values of all pixel points in the background image; ordering the brightness drift factors corresponding to all the pixel points according to the brightness drift factors corresponding to all the pixel points in the background image; determining a value corresponding to the first percentile and a value corresponding to the second percentile in the sorted brightness drift factors; wherein the first percentile is less than the second percentile; according to the value corresponding to the first percentile and/or the distribution interval of the value corresponding to the second percentile, determining the display parameter value corresponding to the observed image;
And adjusting the brightness of the observation image according to the display parameter value corresponding to the observation image.
2. The method of claim 1, wherein separating a background map from the observed image comprises:
separating a green channel image corresponding to the observation image from the observation image;
calculating the mean value and standard deviation of each pixel point in the green channel image; wherein, the pixel point in the green channel image includes: sampling points and non-sampling points;
and separating a background image corresponding to the green channel image from the green channel image according to the mean value and standard deviation of each pixel point in the green channel image and the pixel value of each pixel point.
3. The method of claim 2, wherein the calculating the mean and standard deviation of each pixel in the green channel image comprises:
calculating the mean value and standard deviation of each sampling point in the green channel image according to the predetermined window size corresponding to each sampling point;
and calculating the mean value and the standard deviation of each non-sampling point in the green channel according to the mean value and the standard deviation of each sampling point in the green channel.
4. A method according to claim 3, characterized in that before said calculating the mean and standard deviation of the respective sampling points in the green channel image, the method further comprises:
sampling the observation image to obtain each sampling point of the observation image;
and determining the size of a window corresponding to each sampling point according to the distance between each sampling point and the center point in the observed image.
5. The method according to claim 2, wherein the separating the background image corresponding to the green channel image from the green channel image according to the mean and standard deviation of each pixel point and the pixel value of each pixel point includes:
extracting a pixel point from the pixel points in the green channel image as a current pixel point, and calculating the mahalanobis distance corresponding to the current pixel point according to the mean value and standard deviation of the current pixel point and the pixel value of the current pixel point;
if the mahalanobis distance corresponding to the current pixel point is smaller than or equal to a preset threshold value, the current pixel point is used as one pixel point in the background image corresponding to the green channel image; if the mahalanobis distance corresponding to the current pixel point is larger than the preset threshold value, the current pixel point is used as one pixel point in the foreground image corresponding to the green channel image; and repeatedly executing the operation until each pixel point in the green channel image is determined as the pixel point in the background image or the pixel point in the foreground image.
6. The method according to claim 1, wherein determining the display parameter value corresponding to the observed image for the distribution interval of the values corresponding to the first percentile and/or the values corresponding to the second percentile comprises:
if the value corresponding to the second percentile is smaller than the first value, determining the display parameter value corresponding to the observed image as one display parameter value in the first display parameter value set;
if the value corresponding to the first percentile is larger than the second value, determining the display parameter value corresponding to the observation image as one display parameter value in a second display parameter value set;
if the value corresponding to the first percentile is smaller than the third value and the value corresponding to the second percentile is larger than the fourth value, determining the display parameter value corresponding to the observed image as one display parameter value in a third display parameter value set; wherein the first value is less than the fourth value; the fourth value is less than the third value; the third value is less than the second value.
7. The method according to claim 1, wherein adjusting the brightness of the observed image according to the display parameter value corresponding to the observed image comprises:
Calculating a brightness gain matrix of the observation image according to the display parameter value corresponding to the observation image;
and adjusting the brightness of the observed image according to the brightness gain matrix.
8. The method of claim 7, wherein calculating a brightness gain matrix of the observed image based on the display parameter values corresponding to the observed image comprises:
converting the observed image from the red, green, and blue color space to a hue saturation brightness space;
adjusting the brightness of each pixel point in the brightness channel image corresponding to the observed image by using the display parameter value corresponding to the observed image;
and determining a brightness gain matrix of the observed image according to the brightness value of each pixel point in the brightness channel image after adjustment and the brightness value before adjustment.
9. An apparatus for adjusting brightness of an image, the apparatus comprising: the device comprises an acquisition module, a separation module, a determination module and an adjustment module; wherein,
the acquisition module is used for acquiring an observation image; wherein the observed image is an image of red, green and blue color space;
the separation module is used for separating a background image corresponding to the observation image from the observation image;
The determining module is used for determining a display parameter value corresponding to the observation image according to the background image; wherein the determining module comprises: the second calculation submodule and the determination submodule; the second calculation sub-module is used for calculating brightness drift factors corresponding to all pixel points in the background image according to the predetermined window size and the pixel values of all pixel points in the background image; the determination submodule is used for sequencing the brightness drift factors corresponding to all the pixel points according to the brightness drift factors corresponding to all the pixel points in the background image; determining a value corresponding to the first percentile and a value corresponding to the second percentile in the sorted brightness drift factors; wherein the first percentile is less than the second percentile; according to the value corresponding to the first percentile and/or the distribution interval of the value corresponding to the second percentile, determining the display parameter value corresponding to the observed image;
the adjusting module is used for adjusting the brightness of the observation image according to the display parameter value corresponding to the observation image.
10. The apparatus of claim 9, wherein the separation module comprises: a separation sub-module and a first calculation sub-module; wherein,
The separation submodule is used for separating a green channel image corresponding to the observation image from the observation image;
the first computing submodule is used for computing the mean value and standard deviation of each pixel point in the green channel image; wherein, the pixel point in the green channel image includes: sampling points and non-sampling points;
the separation submodule is further used for separating a background image corresponding to the green channel image from the green channel image according to the mean value and standard deviation of each pixel point in the green channel image and the pixel value of each pixel point.
11. The apparatus according to claim 10, wherein:
the first calculating submodule is specifically used for calculating the mean value and standard deviation of each sampling point in the green channel image according to the window size corresponding to each predetermined sampling point; and calculating the mean value and the standard deviation of each non-sampling point in the green channel according to the mean value and the standard deviation of each sampling point in the green channel.
12. The apparatus according to claim 11, wherein:
the determining module is further used for sampling the observation image to obtain each sampling point of the observation image; and determining the size of a window corresponding to each sampling point according to the distance between each sampling point and the center point in the observed image.
13. The apparatus according to claim 10, wherein:
the separation submodule is specifically configured to extract a pixel point from pixel points in the green channel image as a current pixel point, and calculate a mahalanobis distance corresponding to the current pixel point according to a mean value and a standard deviation of the current pixel point and a pixel value of the current pixel point; if the mahalanobis distance corresponding to the current pixel point is smaller than or equal to a preset threshold value, the current pixel point is used as one pixel point in the background image corresponding to the green channel image; if the mahalanobis distance corresponding to the current pixel point is larger than the preset threshold value, the current pixel point is used as one pixel point in the foreground image corresponding to the green channel image; and repeatedly executing the operation until each pixel point in the green channel image is determined as the pixel point in the background image or the pixel point in the foreground image.
14. The apparatus according to claim 9, wherein:
the determining submodule is specifically configured to determine a display parameter value corresponding to the observed image as one display parameter value in the first display parameter value set if the value corresponding to the second percentile is smaller than the first value; if the value corresponding to the first percentile is larger than the second value, determining the display parameter value corresponding to the observation image as one display parameter value in a second display parameter value set; if the value corresponding to the first percentile is smaller than the third value and the value corresponding to the second percentile is larger than the fourth value, determining the display parameter value corresponding to the observed image as one display parameter value in a third display parameter value set; wherein the first value is less than the fourth value; the fourth value is less than the third value; the third value is less than the second value.
15. The apparatus of claim 9, wherein the adjustment module comprises: a third calculation sub-module and an adjustment sub-module; wherein,
the third calculation sub-module is used for calculating a brightness gain matrix of the observed image according to the display parameter value corresponding to the observed image;
and the adjustment submodule is used for adjusting the brightness of the observation image according to the brightness gain matrix.
16. The apparatus according to claim 15, wherein:
the third calculation sub-module is specifically configured to convert the observed image from the red, green and blue color space to a hue saturation brightness space; adjusting the brightness of each pixel point in the brightness channel image corresponding to the observed image by using the display parameter value corresponding to the observed image; and determining a brightness gain matrix of the observed image according to the brightness value of each pixel point in the brightness channel image after adjustment and the brightness value before adjustment.
17. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.
18. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-8.
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