CN112348763A - Image enhancement method, device, electronic equipment and medium - Google Patents
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Abstract
The embodiment of the application discloses an image enhancement method, an image enhancement device, electronic equipment and a medium. The method comprises the following steps: determining an upper platform threshold and a lower platform threshold of the double-platform histogram equalization according to the mean value and the standard deviation of the histogram value corresponding to the gray value of the basic layer image, and performing double-platform histogram equalization processing on the basic layer image to obtain a target basic layer image; performing gain processing on the detail layer image based on the gain coefficient, and performing segmentation mapping processing based on a segmentation function to obtain a target detail layer image; fusing the target base layer image and the target detail layer image to obtain an enhanced target image; the base layer image and the detail layer image are obtained by filtering the target image. By the scheme, the problem of excessive stretching of the background can be effectively solved, and the details of the image are enhanced while the noise of the image is prevented from being excessively enhanced.
Description
Technical Field
The embodiment of the application relates to the technical field of image processing, in particular to an image enhancement method, an image enhancement device, electronic equipment and a medium.
Background
Digital image Detail Enhancement (DDE) technology is an image processing technology rapidly developed in recent years, and a DDE algorithm improves the capability of detecting and identifying tiny targets by enhancing the contrast between the targets and the background in a scene and the contrast of fine structures (such as edges, contours, textures and the like) on the surfaces of the targets, enhances the accuracy of understanding image content and key Detail information, and can provide a basis for human eye judgment or subsequent automatic identification and tracking.
The current image enhancement technology can not solve the problem of excessive stretching of the background when processing images with concentrated gray levels and small gray value change range, and the applicability of the scene is poor. And the problems of image noise enhancement, partial detail loss and the like exist.
Disclosure of Invention
Embodiments of the present invention provide an image enhancement method, an image enhancement device, an electronic apparatus, and a medium, so as to enhance image details and improve applicability to different images without enhancing noise.
In one embodiment, an embodiment of the present application provides an image enhancement method, including:
determining a mean value and a standard deviation of a gray value corresponding to a histogram value in a histogram of a base layer image, and determining an upper platform threshold value and a lower platform threshold value of double-platform histogram equalization according to the mean value and the standard deviation;
based on the upper platform threshold value and the lower platform threshold value, carrying out double-platform histogram equalization processing on the base layer image to obtain a target base layer image;
performing gain processing on the detail layer image based on the gain coefficient, and performing segmented mapping processing on the gained detail layer image based on a segmented function to obtain a target detail layer image;
fusing the target base layer image and the target detail layer image to obtain an enhanced target image;
the base layer image and the detail layer image are obtained by layering a target image.
In another embodiment, the present application further provides an image enhancement apparatus, including:
the threshold value determining module is used for determining the mean value and the standard deviation of the histogram values corresponding to the gray values in the histogram of the base layer image, and determining the upper platform threshold value and the lower platform threshold value of the double-platform histogram equalization according to the mean value and the standard deviation;
the equalization processing module is used for carrying out double-platform histogram equalization processing on the basic layer image based on the upper platform threshold value and the lower platform threshold value to obtain a target basic layer image;
the detail layer processing module is used for carrying out gain processing on the detail layer image based on the gain coefficient and carrying out sectional mapping processing on the gained detail layer image based on a sectional function to obtain a target detail layer image;
the fusion module is used for fusing the target base layer image and the target detail layer image to obtain an enhanced target image;
the base layer image and the detail layer image are obtained by layering a target image.
In another embodiment, an embodiment of the present application further provides an electronic device, including: one or more processors;
a memory for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the image enhancement method according to any one of the embodiments of the present application.
In yet another embodiment, the present application further provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the image enhancement method according to any one of the embodiments of the present application.
In the embodiment of the application, the mean value and the standard deviation of the histogram value corresponding to the gray value in the histogram of the base layer image are determined, and the upper platform threshold value and the lower platform threshold value of the double-platform histogram equalization are determined according to the mean value and the standard deviation; and based on the upper platform threshold and the lower platform threshold, performing double-platform histogram equalization processing on the base layer image to obtain a target base layer image, so that the upper and lower platform thresholds are adaptively determined to perform equalization processing, the problem of overstretching an image background with a small dynamic range is solved, and the method has applicability to images with different characteristics. Performing gain processing on the detail layer image based on the gain coefficient, and performing segmented mapping processing on the gained detail layer image based on the segmented function to obtain a target detail layer image; and fusing the target base layer image and the target detail layer image to obtain an enhanced target image, thereby effectively enhancing image details, avoiding excessive enhanced image noise and realizing ideal image enhancement effect.
Drawings
FIG. 1 is a flowchart of an image enhancement method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating an implementation of an image enhancement method according to an embodiment of the present invention;
FIG. 3 is a flowchart of an image enhancement method according to another embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a determination of an interval histogram according to another embodiment of the present invention;
FIG. 5 is a flowchart of an image enhancement method according to another embodiment of the present invention;
FIG. 6 is an expanded view of a detail layer image according to another embodiment of the present invention;
FIG. 7 is a schematic diagram of a detail layer image segmentation map according to yet another embodiment of the present invention;
FIG. 8 is a schematic structural diagram of an image enhancement apparatus according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Fig. 1 is a flowchart of an image enhancement method according to an embodiment of the present invention. The image enhancement method provided by the embodiment can be applied to the situation of performing enhancement processing on the image. The method may be specifically executed by an image enhancement apparatus, which may be implemented by software and/or hardware, and the apparatus may be integrated in an electronic device capable of implementing the image enhancement method provided in the embodiment of the present application. Referring to fig. 1, the method of the embodiment of the present application specifically includes:
s110, determining the mean value and the standard deviation of the gray value corresponding to the histogram value in the histogram of the base layer image, and determining the upper platform threshold value and the lower platform threshold value of the double-platform histogram equalization according to the mean value and the standard deviation.
In this embodiment of the application, as shown in fig. 2, for a target image to be enhanced, a layering process is first performed to obtain a base layer image and a detail layer image, where the layering process may be: and performing low-pass filtering on the target image to obtain a basic layer image, and removing the basic layer image from the target image to obtain a detail layer image. Or performing smoothing filtering processing on the target image to obtain a base layer image, and performing sharpening filtering processing on the target image to obtain a detail layer image. The base layer image and the base layer image may be obtained in other ways, and are not limited in particular. The base layer image and the detail layer image are obtained through layered processing, so that the base layer image and the detail layer image are processed respectively, and different modes are adopted for pertinence of background and detail, so that the image enhancement effect is improved. In the histogram of the base layer image, the abscissa is a gray value, and the ordinate is the number of pixel points corresponding to each gray value in the base layer image.
The existing determination mode of the upper and lower platform thresholds of the dual-platform histogram equalization is single and fixed, the upper and lower platform thresholds suitable for the image cannot be adaptively determined according to the characteristics of different images to perform the histogram equalization, and the histogram equalization effect is not ideal. In the embodiment of the application, the upper and lower plateau thresholds are determined in a targeted manner in combination with the histogram mean and standard deviation of the base layer image, so that noise can be effectively suppressed, and details can be amplified and enhanced moderately.
In the embodiment of the present application, the upper plateau threshold may be the sum of the mean and the standard deviation, and the lower plateau threshold may be the difference between the mean and the standard deviation. The upper platform threshold and the lower platform threshold may also be determined according to other relations between the mean and the standard deviation, which is not limited herein, and any scheme that determines the upper platform threshold and the lower platform threshold according to the mean and the standard deviation is within the protection scope of the present application.
And S120, based on the upper platform threshold value and the lower platform threshold value, carrying out double-platform histogram equalization processing on the basic layer image to obtain a target basic layer image.
In the embodiment of the present application, a scheme for performing dual-platform histogram equalization processing on a base layer image based on an upper platform threshold and a lower platform threshold includes: and correcting the histogram value corresponding to each gray value in the histogram of the basic layer by adopting an upper platform threshold value and a lower platform threshold value, correcting the histogram value into the lower platform threshold value if the histogram value is less than or equal to the lower platform threshold value, keeping the histogram value unchanged if the histogram value is greater than the lower platform threshold value and less than the upper platform threshold value, and correcting the histogram value into the upper platform threshold value if the histogram value is greater than or equal to the upper platform threshold value. And calculating the cumulative histogram value corresponding to each gray value according to the corrected histogram value, and redistributing the gray values based on the cumulative histogram value corresponding to each gray value, the total pixel number of the base layer image and the gray level mapping range to distribute the gray level mapping between the gray level mapping ranges. And mapping the gray value of each pixel point in the base layer image based on the mapping relation of the gray value so as to distribute the gray value mapping of each pixel point of the base layer image in the gray level mapping range to obtain the target base layer image.
S130, performing gain processing on the detail layer image based on the gain coefficient, and performing segmented mapping processing on the gained detail layer image based on the segmented function to obtain a target detail layer image.
Wherein the gain factor can be determined according to actual conditions. And multiplying the gray value of each pixel point of the detail layer image by a gain coefficient to obtain the gained detail layer image, thereby realizing the enhancement processing of the details of the detail layer image.
If the gain processing is performed on the whole detail layer image, the gain processing is performed on the noise in the detail layer image at the same time, so that in the embodiment of the application, after the gain processing is performed on the detail layer image, the segmented mapping processing is performed on the gained detail layer image based on the segmented function to weaken noise pixel points and strengthen the detail points. The piecewise function can be adaptively determined according to actual conditions, the gray values of the noise points smaller than the lower limit of the preset gray value and the noise points larger than the upper limit of the preset gray value can be set as constants, and the gray values of the detail points between the lower limit of the preset gray value and the upper limit of the preset gray value are gained, so that the characteristics of the detail points are highlighted.
S140, fusing the target basic layer image and the target detail layer image to obtain an enhanced target image.
Illustratively, the target base layer image and the target detail layer image are added and fused to obtain an enhanced target image, so as to enhance the target image.
In the embodiment of the application, the mean value and the standard deviation of the histogram value corresponding to the gray value in the histogram of the base layer image are determined, and the upper platform threshold value and the lower platform threshold value of the double-platform histogram equalization are determined according to the mean value and the standard deviation; based on the upper platform threshold and the lower platform threshold, performing double-platform histogram equalization processing on the base layer image to obtain a target base layer image, and adaptively determining the upper platform and the lower platform to perform equalization processing with the upper platform and the lower platform, so that the problem of overstretching an image background with a small dynamic range is solved, and the method has applicability to different images. Performing gain processing on the detail layer image based on the gain coefficient, and performing segmented mapping processing on the gained detail layer image based on the segmented function to obtain a target detail layer image; and fusing the target base layer image and the target detail layer image to obtain an enhanced target image, thereby effectively enhancing image details, avoiding excessive enhanced image noise and realizing ideal image enhancement effect.
Fig. 3 is a flowchart of an image enhancement method according to another embodiment of the present invention. For further optimization of the embodiments, details which are not described in detail in the embodiments are described in the embodiments. Referring to fig. 3, the image enhancement method provided by the present embodiment may include:
s210, determining a median gray value corresponding to a median of the total pixel number or a median gray value corresponding to a mean of the total pixel number in the histogram of the basic layer image.
Illustratively, a median value of the total pixel number of the base layer image is determined, and in the histogram of the base layer image, a gray value corresponding to the median value is determined as a median gray value. Or, an average value of the total pixel numbers of the base layer image may be determined, and a gray value corresponding to the average value is determined as a median gray value in a histogram of the base layer image. As shown in fig. 4, in the ordinate of the base layer image histogram, the ordinate corresponding to the median of the total pixel count is locked, and the abscissa ranged mid corresponding to the ordinate is determined, that is, the median gray value is obtained.
S220, determining a first mean value and a first standard deviation of the histogram value according to a first interval histogram corresponding to the gray value interval from the minimum gray level mapping range value to the median gray value.
Illustratively, the grayscale mapping range is [ ranging down, ranging up ], the minimum value of the grayscale mapping range is ranging down, the maximum value of the grayscale mapping range is ranging up, and a grayscale value interval from the minimum value of the grayscale mapping range to a median grayscale value is [ ranging down, ranging mid ], so that the histogram of a first interval corresponding to the interval is the histogram of the S1 part in fig. 4. A first mean and a first standard deviation of the histogram values are determined according to S1. The first mean is the sum of all the ordinates in S1 divided by (ranging mid-ranging down). The first standard deviation is calculated for the histogram values of the points in S1 and the first mean value.
And S230, determining a second mean value and a second standard deviation of the histogram value according to a second interval histogram corresponding to the gray value interval from the median gray value to the maximum value of the gray level mapping range.
Illustratively, the gray value interval from the median gray value to the maximum value of the gray level mapping range is [ ranging mid, ranging up ], and a histogram of a second interval corresponding to the interval is a histogram of the portion S2 in fig. 4. A second mean and a second standard deviation of the histogram values are determined according to S2. The second mean is the sum of all the ordinates in S2 divided by (ranging-ranging mid), and then a second standard deviation is calculated for the histogram values of each point in S2 and the second mean.
S240, determining the upper platform threshold value and the lower platform threshold value according to the first mean value, the first standard deviation, the second mean value and the second standard deviation.
Illustratively, the upper plateau threshold and the lower plateau threshold are determined according to the first mean, the first standard deviation, the second mean and the second standard deviation based on the following formulas:
wherein, T1To upper plateau threshold, T2The mean1 is a first mean value, mean2 is a second mean value, std1 is a first standard deviation, std2 is a second standard deviation, and k is more than or equal to 0 and less than or equal to 3. The parameter k can be adjusted to adjust an upper platform threshold value and a lower platform threshold value, and the experimental result of researchers in the application is that when k is 1.5, the obtained upper platform threshold value and the obtained lower platform threshold value have a good effect on equalization processing of the base layer image.
And S250, carrying out double-platform histogram equalization processing on the basic layer image based on the upper platform threshold value and the lower platform threshold value to obtain a target basic layer image.
Illustratively, the upper plateau threshold T to be obtained1And lower plateau threshold T2The statistical histogram value P after the double-platform correction can be obtained by being brought into a double-platform threshold correction formulaT(k)：
Wherein P isT(k) The histogram value of the double-platform showing the basic layer image, namely the histogram value of the basic layer image after the double-platform adjustment; h (k) represents the histogram values of the base layer image without the dual-plateau adjustment.
According to the histogram value PT(k) The cumulative histogram C corresponding to the gray value k can be calculatedT(k)：
Wherein L-1 is the maximum gray scale value.
C obtained by the above formulaT(k) The gray level of the image is redistributed, and a gray level mapping table D which is finally equalized can be obtainedT(k)：
Wherein
To round the symbol, R is the gray level mapping range.
Using grey value mapping table DT(k) Carrying out gray value mapping on the pixel value of the base layer image BaseX to obtain the mapped pixel value of the base layer image:
BaseY(i,j)＝DT(BaseX(i,j))
wherein BaseX (i, j) is the gray value of the base layer image at the pixel point (i, j), and BaseY (i, j) is the gray value of the base layer image at the pixel point (i, j).
And S260, performing gain processing on the detail layer image based on the gain coefficient, and performing segmented mapping processing on the gained detail layer image based on the segmented function to obtain a target detail layer image.
S270, fusing the target basic layer image and the target detail layer image to obtain an enhanced target image.
In an embodiment of the present application, the process of determining the gray scale mapping range includes: determining an accumulated histogram value corresponding to each gray value through the histogram of the basic layer image, and determining an upper threshold and a lower threshold according to the total pixel number of the basic layer image; if the cumulative histogram value is greater than or equal to the lower limit threshold and the difference value between the cumulative histogram value and the lower limit threshold is minimum, taking the gray value corresponding to the cumulative histogram value as the minimum of the gray level mapping range; and if the cumulative histogram value is greater than or equal to the upper limit threshold and the difference value between the cumulative histogram value and the upper limit threshold is minimum, taking the gray value corresponding to the cumulative histogram value as the maximum value of the gray level mapping range.
Determining an upper threshold and a lower threshold according to the total number of pixels of the base layer image based on the following formula:
tUp＝M×(1-t)
tDown＝M×t；
wherein tUp is an upper threshold and a lower threshold, tDown is a lower threshold, M is the total number of pixels of the base layer image, and t is more than or equal to 0 and less than or equal to 1.
Wherein t is a proportionality coefficient, the range is [0,1], the smaller t is, the larger t is, the smaller t is, the brightness of the final image is influenced, and the effect of taking 0.15 image as the experimental test t is better.
If the cumulative histogram value is just greater than or equal to tDown, that is, if the cumulative histogram value is greater than or equal to the lower threshold and the difference from the lower threshold is the smallest among all the cumulative histogram values greater than or equal to the lower threshold, the gray value rangeDown corresponding to the cumulative histogram value is recorded. If the cumulative histogram value is just greater than or equal to tUp, that is, the cumulative histogram value is greater than or equal to the upper threshold, and the difference between the cumulative histogram value and the upper threshold is the smallest among all the cumulative histogram values greater than or equal to the upper threshold, the gray level rangeUp corresponding to the cumulative histogram value is recorded, and the final gray level mapping range is R, which is calculated by the following formula:
wherein the range is a range up-range down.
The method has the advantages that the upper limit threshold value and the lower limit threshold value are adaptively determined according to the total pixel number, and the gray level mapping range is determined according to the actual accumulated histogram characteristics, so that the gray level mapping range suitable for mapping the basic layer image can be determined in a targeted manner by considering the gray level characteristics of the basic layer image, and the problem of excessive stretching of the background caused by adopting a single fixed gray level mapping range or an unsuitable gray level mapping range for image mapping is avoided.
According to the scheme in the embodiment of the application, the upper platform threshold value and the lower platform threshold value are calculated according to the mean value and the standard deviation of the two parts of histograms, so that the upper platform threshold value and the lower platform threshold value can be adaptively determined by combining the characteristics of the histograms, a better effect is achieved when equalization is carried out based on the upper platform threshold value and the lower platform threshold value, noise points are effectively suppressed, and minutiae are enhanced.
Fig. 5 is a flowchart of an image enhancement method according to another embodiment of the present invention. For further optimization of the embodiments, details which are not described in detail in the embodiments are described in the embodiments. Referring to fig. 5, the image enhancement method provided by the present embodiment may include:
s310, determining the mean value and the standard deviation of the gray value corresponding to the histogram value in the histogram of the base layer image, and determining the upper platform threshold value and the lower platform threshold value of the double-platform histogram equalization according to the mean value and the standard deviation.
And S320, carrying out double-platform histogram equalization processing on the basic layer image based on the upper platform threshold value and the lower platform threshold value to obtain a target basic layer image.
S330, traversing the detail layer image by taking each pixel point in the detail layer image as the center of the preset traversal matrix based on the preset traversal matrix, and determining the variance of the gray value of each pixel point.
Illustratively, the edge expansion processing is performed on the detail layer image according to the specification of the preset traversal matrix, so that each pixel point can be located at the center of the preset traversal matrix. For example, if the preset traversal matrix is a 3 × 3 matrix, a layer of pixels is expanded outside the detail layer image, so that each pixel can be located in the center of the preset traversal matrix. As shown in fig. 6, the shadow portion is a detail layer image, the dotted line portion is an extension pixel point, the gray value of the extension pixel point may be the same as the gray value of the pixel point of the adjacent detail layer image, or all of the extension pixel points may be set to 0, which may be set according to an actual situation, and is not limited herein. If the radius of the predetermined traversal matrix is N units (in pixels), N units need to be extended. And traversing the detail layer image subjected to edge expansion from left to right from top to bottom pixel by adopting a preset traversal matrix, and calculating the variance sigma (i, j) of the gray value of a pixel point in the preset traversal matrix with the current pixel position (i, j) as the center.
S340, determining the variance mean value of each pixel point in the detail layer image.
Specifically, for each pixel point in the detail layer image, after traversing through a preset traversal matrix, a variance is correspondingly obtained, and a variance mean of the variances corresponding to each pixel point is calculated.
And S350, determining a gain coefficient according to the variance and the variance mean.
Determining the gain factor based on the following equation:
wherein f (i, j) is the gain coefficient corresponding to the pixel point (i, j), σ (i, j) is the variance,
mean of variance, θ is an adjustable parameter, default to 1.
And S360, performing gain processing on the detail layer image based on the gain coefficient.
Performing gain processing on the detail layer image based on the following formula:
detailImg2(i,j)＝detailImg1(i,j)×f(i,j)
wherein, the detail layer image gray value after the gain processing is adopted as the detail layer image deciilimg 2(i, j), and the detail layer image gray value before the gain processing is adopted as the detail layer image deciilimg 1(i, j).
The method has the advantages that the edge degree of the local area is judged according to the local variance in the image of the detail layer, the local gain coefficient is further calculated according to the edge degree, the gain processing is carried out on the detail layer, the edge details in the detail layer can be reserved through the processing of the detail layer through the self-adaptive gain, and the noise is suppressed, so that the signal-to-noise ratio of the image is improved.
And S370, performing segmented mapping processing on the gained detail layer image based on the segmented function to obtain a target detail layer image.
And performing segmented mapping processing on the gained detail layer image based on the following segmented functions to obtain a target detail layer image:
the method comprises the steps of obtaining a detail layer image by using a segmentation mapping process, wherein core, th1 and th2 are preset gray value thresholds, core < th1< th2, k1, b1, k2 and b2 are preset parameters, DETAILX is the gray value of each pixel point of the detail layer image before the segmentation mapping process, and DETAILY is the gray value of each pixel point of the detail layer image after the segmentation mapping process.
Fig. 7 is an exemplary case of the piecewise function, where the abscissa in the coordinate system is DetailX to represent the absolute value of the grayscale value of the detail layer image detailImg2 after the gain processing, maxX is the maximum value of the grayscale value of the detail layer image, the ordinate DetailY represents the grayscale value finally output by the detail layer image, and the maximum value maxY of the output grayscale value is the maximum grayscale level displayable by the display, for example 255. The parameter core can suppress noise, the noise suppression is more obvious when the core is larger, but the edge detail is lost when the core is too large. The parameter th1 controls the degree of sharpening of medium details, increasing th1 and Y1 can enhance medium details, the parameter th2 controls the degree of sharpening of strong edges, and increasing th2 can enhance edges. In fig. 7, k1> k2 may be k1< k2, and is not specifically limited herein, and specific values of k1, b1, k2, and b2 are not specifically limited, and it is within the scope of the present application as long as the detail layer image after the gain is subjected to the segment mapping processing according to the function.
And S380, fusing the target basic layer image and the target detail layer image to obtain an enhanced target image.
Illustratively, the target base layer image and the target detail layer image may be fused to obtain an enhanced target image based on the following formula:
OutImg＝α×BaseY+β×DetailY
BaseY is a target base layer image, DetailY is a target detail layer image, alpha is a brightness enhancement factor, beta is a detail enhancement factor, the larger alpha is the image brightness, the larger beta is the image detail degree, alpha is 1.5, and beta is 3 in the embodiment.
In the embodiment of the application, local details can be highlighted by calculating the local gain coefficient according to the local edge degree and performing gain processing on the detail layer, noise can be effectively suppressed by mapping the gray value of the image of the detail layer through the piecewise linear mapping function, and the edge-preserving and noise-reducing effects of the image of the detail layer are effectively improved by combining the local gain coefficient and the gray value.
Fig. 8 is a schematic structural diagram of an image enhancement apparatus according to an embodiment of the present invention. The device is applicable to the situation of carrying out enhancement processing on the image. The apparatus may be implemented by software and/or hardware, and the apparatus may be integrated in an electronic device. Referring to fig. 8, the apparatus specifically includes:
a threshold determining module 410, configured to determine a mean value and a standard deviation of histogram values corresponding to gray values in a histogram of the base layer image, and determine an upper platform threshold and a lower platform threshold of dual-platform histogram equalization according to the mean value and the standard deviation;
the equalization processing module 420 is configured to perform double-platform histogram equalization processing on the base layer image based on the upper platform threshold and the lower platform threshold to obtain a target base layer image;
the detail layer processing module 430 is configured to perform gain processing on the detail layer image based on the gain coefficient, and perform segmented mapping processing on the gained detail layer image based on a piecewise function to obtain a target detail layer image;
a fusion module 440, configured to fuse the target base layer image and the target detail layer image to obtain an enhanced target image;
the base layer image and the detail layer image are obtained by layering a target image.
In an embodiment of the present application, the threshold determining module 410 includes:
a suspension gray value determining unit, configured to determine a median gray value corresponding to a median of the total pixel numbers or a median gray value corresponding to a mean of the total pixel numbers in the histogram of the base layer image;
the first determining unit is used for determining a first mean value and a first standard deviation of a corresponding histogram value according to a first interval histogram corresponding to a gray value interval from the minimum gray level mapping range value to the median gray value;
the second determining unit is used for determining a second mean value and a second standard deviation of the histogram value according to a second interval histogram corresponding to the gray value interval from the median gray value to the maximum value of the gray level mapping range;
a threshold determining unit, configured to determine the upper plateau threshold and the lower plateau threshold according to the first mean, the first standard deviation, the second mean, and the second standard deviation.
In an embodiment of the present application, the threshold determining unit is specifically configured to:
determining the upper plateau threshold and the lower plateau threshold from the first mean, the first standard deviation, the second mean, and the second standard deviation based on the following formulas:
wherein, T1To upper plateau threshold, T2The mean1 is a first mean value, mean2 is a second mean value, std1 is a first standard deviation, std2 is a second standard deviation, and k is more than or equal to 0 and less than or equal to 3.
In an embodiment of the present application, the apparatus further includes:
a gray level mapping range determining module for determining a gray level mapping range by:
determining an accumulated histogram value corresponding to each gray value through the histogram of the basic layer image, and determining an upper threshold and a lower threshold according to the total pixel number of the basic layer image;
if the cumulative histogram value is greater than or equal to the lower limit threshold and the difference value between the cumulative histogram value and the lower limit threshold is minimum, taking the gray value corresponding to the cumulative histogram value as the minimum of the gray level mapping range;
and if the cumulative histogram value is greater than or equal to the upper limit threshold and the difference value between the cumulative histogram value and the upper limit threshold is minimum, taking the gray value corresponding to the cumulative histogram value as the maximum value of the gray level mapping range.
In an embodiment of the present application, the gray scale mapping range determining module is specifically configured to:
determining an upper threshold and a lower threshold according to the total number of pixels of the base layer image based on the following formula:
tUp＝M×(1-t)
tDown＝M×t；
wherein tUp is an upper threshold and a lower threshold, tDown is a lower threshold, M is the total number of pixels of the base layer image, and t is more than or equal to 0 and less than or equal to 1.
In this embodiment, the detail layer processing module 430 includes:
the variance determining unit is used for traversing the detail layer image by taking each pixel point in the detail layer image as the center of the preset traversal matrix based on a preset traversal matrix, and determining the variance of the gray value of each pixel point;
the variance mean determining unit is used for determining the variance mean of each pixel point in the detail layer image;
and the gain coefficient determining unit is used for determining a gain coefficient according to the variance and the variance mean.
In this embodiment of the application, the detail layer processing module 430 is specifically configured to:
determining the gain factor based on the following equation:
wherein f (i, j) is the gain coefficient corresponding to the pixel point (i, j), σ (i, j) is the variance,
mean of variance, θ is an adjustable parameter.
In this embodiment of the application, the detail layer processing module 430 is specifically configured to:
and performing segmented mapping processing on the gained detail layer image based on the following segmented functions to obtain a target detail layer image:
the method comprises the steps of obtaining a detail layer image by using a segmentation mapping process, wherein core, th1 and th2 are preset gray value thresholds, core < th1< th2, k1, b1, k2 and b2 are preset parameters, DETAILX is the gray value of each pixel point of the detail layer image before the segmentation mapping process, and DETAILY is the gray value of each pixel point of the detail layer image after the segmentation mapping process.
The image enhancement device provided by the embodiment of the application can execute the image enhancement method provided by any embodiment of the application, and has corresponding functional modules and beneficial effects of the execution method.
Fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. FIG. 9 illustrates a block diagram of an exemplary electronic device 512 suitable for use in implementing embodiments of the present application. The electronic device 512 shown in fig. 9 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 9, the electronic device 512 may include: one or more processors 516; the memory 528 is configured to store one or more programs, and when the one or more programs are executed by the one or more processors 516, the one or more processors 516 may implement the image enhancement method provided in the embodiment of the present application, including:
determining a mean value and a standard deviation of a gray value corresponding to a histogram value in a histogram of a base layer image, and determining an upper platform threshold value and a lower platform threshold value of double-platform histogram equalization according to the mean value and the standard deviation;
based on the upper platform threshold value and the lower platform threshold value, carrying out double-platform histogram equalization processing on the base layer image to obtain a target base layer image;
performing gain processing on the detail layer image based on the gain coefficient, and performing segmented mapping processing on the gained detail layer image based on a segmented function to obtain a target detail layer image;
fusing the target base layer image and the target detail layer image to obtain an enhanced target image;
the base layer image and the detail layer image are obtained by layering a target image.
Components of the electronic device 512 may include, but are not limited to: one or more processors or processors 516, a memory 528, and a bus 518 that couples the various device components, including the memory 528 and the processors 516.
The electronic device 512 typically includes a variety of computer device-readable storage media. These storage media may be any available storage media that can be accessed by electronic device 512 and includes both volatile and nonvolatile storage media, removable and non-removable storage media.
The memory 528 may include computer device readable storage media in the form of volatile memory, such as Random Access Memory (RAM)530 and/or cache memory 532. The electronic device 512 may further include other removable/non-removable, volatile/nonvolatile computer device storage media. By way of example only, storage system 534 may be used to read from and write to non-removable, nonvolatile magnetic storage media (not shown in FIG. 9, and commonly referred to as a "hard drive"). Although not shown in FIG. 9, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical storage medium) may be provided. In these cases, each drive may be connected to bus 418 by one or more data storage media interfaces. Memory 528 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 540 having a set (at least one) of program modules 542 may be stored, for example, in memory 528, such program modules 542 including, but not limited to, an operating device, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may include an implementation of a network environment. The program modules 542 generally perform the functions and/or methods of the described embodiments of the invention.
The electronic device 512 may also communicate with one or more external devices 514 (e.g., keyboard, pointing device, display 524, etc.), with one or more devices that enable a user to interact with the electronic device 512, and/or with any devices (e.g., network card, modem, etc.) that enable the electronic device 512 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 522. Also, the electronic device 512 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) through the network adapter 420. As shown in FIG. 9, the network adapter 520 communicates with the other modules of the electronic device 512 via the bus 518. It should be appreciated that although not shown in FIG. 9, other hardware and/or software modules may be used in conjunction with the electronic device 512, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID devices, tape drives, and data backup storage devices, among others.
The processor 516 executes various functional applications and data processing by executing at least one of other programs of the programs stored in the memory 528, for example, to implement an image enhancement method provided by the embodiment of the present application.
One embodiment of the present invention provides a storage medium containing computer-executable instructions which, when executed by a computer processor, perform a method of image enhancement, comprising:
determining a mean value and a standard deviation of a gray value corresponding to a histogram value in a histogram of a base layer image, and determining an upper platform threshold value and a lower platform threshold value of double-platform histogram equalization according to the mean value and the standard deviation;
based on the upper platform threshold value and the lower platform threshold value, carrying out double-platform histogram equalization processing on the base layer image to obtain a target base layer image;
performing gain processing on the detail layer image based on the gain coefficient, and performing segmented mapping processing on the gained detail layer image based on a segmented function to obtain a target detail layer image;
fusing the target base layer image and the target detail layer image to obtain an enhanced target image;
the base layer image and the detail layer image are obtained by layering a target image.
The computer storage media of the embodiments of the present application may take any combination of one or more computer-readable storage media. The computer readable storage medium may be a computer readable signal storage medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor device, apparatus, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the present application, a computer readable storage medium may be any tangible storage medium that can contain, or store a program for use by or in connection with an instruction execution apparatus, device, or apparatus.
A computer readable signal storage medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal storage medium may also be any computer readable storage medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution apparatus, device, or apparatus.
Program code embodied on a computer readable storage medium may be transmitted using any appropriate storage medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or device. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (11)
1. A method of image enhancement, the method comprising:
determining a mean value and a standard deviation of a gray value corresponding to a histogram value in a histogram of a base layer image, and determining an upper platform threshold value and a lower platform threshold value of double-platform histogram equalization according to the mean value and the standard deviation;
based on the upper platform threshold value and the lower platform threshold value, carrying out double-platform histogram equalization processing on the base layer image to obtain a target base layer image;
performing gain processing on the detail layer image based on the gain coefficient, and performing segmented mapping processing on the gained detail layer image based on a segmented function to obtain a target detail layer image;
fusing the target base layer image and the target detail layer image to obtain an enhanced target image;
the base layer image and the detail layer image are obtained by layering a target image.
2. The method of claim 1, wherein determining a mean and a standard deviation of histogram values corresponding to gray values in a histogram of the base layer image, and determining an upper plateau threshold and a lower plateau threshold for dual-plateau histogram equalization based on the mean and the standard deviation comprises:
determining a median gray value corresponding to a median of the total pixel number or a median gray value corresponding to a mean of the total pixel number in the histogram of the base layer image;
determining a first mean value and a first standard deviation of a value corresponding to a histogram according to a first interval histogram corresponding to a gray value interval from the minimum gray level mapping range value to the median gray value;
determining a second mean value and a second standard deviation of the histogram value according to a second interval histogram corresponding to the gray value interval from the median gray value to the maximum value of the gray level mapping range;
and determining the upper platform threshold value and the lower platform threshold value according to the first mean value, the first standard deviation, the second mean value and the second standard deviation.
3. The method of claim 2, wherein the upper plateau threshold and the lower plateau threshold are determined from the first mean, the first standard deviation, the second mean, and the second standard deviation based on the following formulas:
wherein, T1To upper plateau threshold, T2The mean1 is a first mean value, mean2 is a second mean value, std1 is a first standard deviation, std2 is a second standard deviation, and k is more than or equal to 0 and less than or equal to 3.
4. The method of claim 2, wherein determining a gray scale mapping range comprises:
determining an accumulated histogram value corresponding to each gray value through the histogram of the basic layer image, and determining an upper threshold and a lower threshold according to the total pixel number of the basic layer image;
if the cumulative histogram value is greater than or equal to the lower limit threshold and the difference value between the cumulative histogram value and the lower limit threshold is minimum, taking the gray value corresponding to the cumulative histogram value as the minimum of the gray level mapping range;
and if the cumulative histogram value is greater than or equal to the upper limit threshold and the difference value between the cumulative histogram value and the upper limit threshold is minimum, taking the gray value corresponding to the cumulative histogram value as the maximum value of the gray level mapping range.
5. The method according to claim 4, wherein the upper threshold and the lower threshold are determined according to the total number of pixels of the base layer image based on the following formula:
tUp＝M×(1-t)
tDown＝M×t；
wherein tUp is an upper threshold and a lower threshold, tDown is a lower threshold, M is the total number of pixels of the base layer image, and t is more than or equal to 0 and less than or equal to 1.
6. The method of claim 1, wherein prior to gain processing the detail layer image based on the gain factor, the method further comprises:
based on a preset traversal matrix, taking each pixel point in the detail layer image as the center of the preset traversal matrix, traversing the detail layer image, and determining the variance of the gray value of each pixel point;
determining the variance mean value of each pixel point in the detail layer image;
and determining a gain coefficient according to the variance and the variance mean.
7. The method of claim 6, wherein the gain factor is determined based on the following equation:
wherein f (i, j) is the gain coefficient corresponding to the pixel point (i, j), σ (i, j) is the variance,
mean of variance, θ is an adjustable parameter.
8. The method according to claim 1, wherein the segmented mapping process is performed on the gained detail layer image based on the following segmentation function to obtain the target detail layer image:
the method comprises the steps of obtaining a detail layer image by using a segmentation mapping process, wherein core, th1 and th2 are preset gray value thresholds, core < th1< th2, k1, b1, k2 and b2 are preset parameters, DETAILX is the gray value of each pixel point of the detail layer image before the segmentation mapping process, and DETAILY is the gray value of each pixel point of the detail layer image after the segmentation mapping process.
9. An image enhancement apparatus, characterized in that the apparatus comprises:
the threshold value determining module is used for determining the mean value and the standard deviation of the histogram values corresponding to the gray values in the histogram of the base layer image, and determining the upper platform threshold value and the lower platform threshold value of the double-platform histogram equalization according to the mean value and the standard deviation;
the equalization processing module is used for carrying out double-platform histogram equalization processing on the basic layer image based on the upper platform threshold value and the lower platform threshold value to obtain a target basic layer image;
the detail layer processing module is used for carrying out gain processing on the detail layer image based on the gain coefficient and carrying out sectional mapping processing on the gained detail layer image based on a sectional function to obtain a target detail layer image;
the fusion module is used for fusing the target base layer image and the target detail layer image to obtain an enhanced target image;
the base layer image and the detail layer image are obtained by layering a target image.
10. An electronic device, characterized in that the electronic device comprises:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the image enhancement method of any one of claims 1-8.
11. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the image enhancement method according to any one of claims 1 to 8.
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