CN113191990A - Image processing method and device, electronic equipment and medium - Google Patents
Image processing method and device, electronic equipment and medium Download PDFInfo
- Publication number
- CN113191990A CN113191990A CN202110590447.9A CN202110590447A CN113191990A CN 113191990 A CN113191990 A CN 113191990A CN 202110590447 A CN202110590447 A CN 202110590447A CN 113191990 A CN113191990 A CN 113191990A
- Authority
- CN
- China
- Prior art keywords
- histogram
- image
- detail
- platform
- cumulative
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000003672 processing method Methods 0.000 title claims abstract description 27
- 230000001186 cumulative effect Effects 0.000 claims abstract description 120
- 238000013507 mapping Methods 0.000 claims abstract description 51
- 238000001914 filtration Methods 0.000 claims abstract description 36
- 238000000034 method Methods 0.000 claims abstract description 23
- 238000009825 accumulation Methods 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 description 7
- 238000004422 calculation algorithm Methods 0.000 description 6
- 230000003287 optical effect Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- 230000006870 function Effects 0.000 description 3
- 230000002708 enhancing effect Effects 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/40—Image enhancement or restoration using histogram techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20024—Filtering details
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
- Facsimile Image Signal Circuits (AREA)
Abstract
The embodiment of the application discloses an image processing method, an image processing device, electronic equipment and a medium. The method comprises the following steps: determining an initial histogram and a detail histogram according to a high-pass filtering image of an image to be processed and the image to be processed; determining a first platform threshold value and a second platform threshold value according to the relation between the first reference threshold value and the statistic value of the detail cumulative histogram and the relation between the second reference threshold value and the statistic value of the detail cumulative histogram; determining a double-platform histogram according to the first platform threshold value, the second platform threshold value and the initial histogram; and determining a gray mapping relation according to the double-platform cumulative histogram, and processing the image to be processed based on the gray mapping relation to obtain a target image. According to the scheme, the details of the image can be considered when the double-platform threshold value is determined, so that the determined double-platform threshold value can be suitable for processing the image under different scenes, and the scene adaptability of the image processing method is improved.
Description
Technical Field
The embodiment of the application relates to the technical field of image processing, in particular to an image processing method, an image processing device, electronic equipment and a medium.
Background
Image processing is a technique in which a computer analyzes an image to achieve a desired result. The image processing includes image transformation, image coding compression, image enhancement and restoration, image segmentation, image description, image classification and the like.
Image enhancement techniques are commonly used for preprocessing images and improving the display quality of the images, and are widely applied to digital image processing systems, wherein image enhancement is usually performed in a spatial domain or a frequency domain, and the most common methods in the spatial domain are linear stretching algorithms, histogram equalization processing, exponential transformation processing and the like. The histogram equalization method is widely applied due to the characteristics of simple algorithm and strong adaptability.
The background and noise occupy too much gray scale range after part of scenes are enhanced because the current histogram equalization algorithm does not distinguish the image background and the target, but the final image effect is poor because information such as target details and the like occupy less gray scale range. Although the improved histogram equalization algorithm can alleviate the problems of excessive background stretching, image detail loss and the like to a certain extent, the existing improved histogram equalization algorithm is difficult to adaptively determine enhancement parameters aiming at different scenes, so that the image enhancement effect is limited.
Disclosure of Invention
The embodiment of the application provides an image processing method, an image processing device, electronic equipment and a medium, so as to improve the processing effect of images in different scenes.
In one embodiment, an embodiment of the present application provides an image processing method, including:
determining an initial histogram according to a high-pass filtering image of an image to be processed and the image to be processed, and determining a detail histogram of the image to be processed according to the initial histogram;
determining a first platform threshold value and a second platform threshold value from the statistics values of the detail cumulative histogram according to the relation between a first reference threshold value and the statistics values of the detail cumulative histogram and the relation between a second reference threshold value and the statistics values of the detail cumulative histogram; wherein the first reference threshold and the second reference threshold are determined according to a maximum statistical value of the cumulative detail histogram, the cumulative detail histogram being a cumulative histogram of the detail histogram;
determining a dual-platform histogram according to the first platform threshold, the second platform threshold and the initial histogram;
determining a gray level mapping relation according to the double-platform cumulative histogram, and processing the image to be processed based on the gray level mapping relation to obtain a target image; wherein the dual-plateau cumulative histogram is a cumulative histogram of the dual-plateau histogram.
In another embodiment, an embodiment of the present application further provides an image processing apparatus, including:
the detail histogram determination module is used for determining an initial histogram according to a high-pass filtering image of an image to be processed and the image to be processed, and determining a detail histogram of the image to be processed according to the initial histogram, wherein the detail cumulative histogram is a cumulative histogram of the detail histogram;
a threshold determination module, configured to determine a first plateau threshold and a second plateau threshold from the statistics of the cumulative detail histogram according to a relationship between a first reference threshold and the statistics of the cumulative detail histogram and a relationship between a second reference threshold and the statistics of the cumulative detail histogram; wherein the first reference threshold and the second reference threshold are determined according to a maximum statistic of the cumulative histogram of details;
a dual-platform histogram determination module, configured to determine a dual-platform histogram according to the first platform threshold, the second platform threshold, and the initial histogram;
the processing module is used for determining a gray mapping relation according to the double-platform cumulative histogram and processing the image to be processed based on the gray mapping relation to obtain a target image; wherein the dual-plateau cumulative histogram is a cumulative histogram of the dual-plateau histogram.
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 processing method according to any one of the embodiments.
In one 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 processing method according to any one of the embodiments of the present application.
In the embodiment of the application, the initial histogram is determined according to the high-pass filtering image of the image to be processed and the image to be processed, and determining a detail histogram of the image to be processed according to the initial histogram, thereby counting high-frequency components of the image to be processed, determining a detail part of the image to be processed, determining a first plateau threshold value and a second plateau threshold value from the statistics of the cumulative detail histogram based on a relationship of a first reference threshold value to the statistics of the cumulative detail histogram and a relationship of a second reference threshold value to the statistics of the cumulative detail histogram, thereby, the detailed part is taken into consideration in the process of determining the first platform threshold and the second platform threshold so as to be suitable for the image to be processed under the current scene, determining a dual-platform histogram according to the first platform threshold, the second platform threshold and the initial histogram; and determining a gray level mapping relation according to the double-platform cumulative histogram, and processing the image to be processed based on the gray level mapping relation to obtain a target image, so that the processing effect of the image to be processed is improved.
Drawings
Fig. 1 is a flowchart of an image processing method according to an embodiment of the present application;
FIG. 2 is a flowchart of an image processing method according to another embodiment of the present application;
FIG. 3 is a flowchart of an image processing method according to another embodiment of the present application;
fig. 4 is a schematic structural diagram of an image processing apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some of the structures related to the present application are shown in the drawings, not all of the structures.
Fig. 1 is a flowchart of an image processing method according to an embodiment of the present application. The image processing method provided by the embodiment of the application can be applied to the situation of processing the image. Typically, the embodiment of the application is suitable for the situation of enhancing the image to be processed based on the dual-platform histogram. The method may be specifically executed by an image processing apparatus, the apparatus may be implemented in a software and/or hardware manner, the apparatus may be integrated in an electronic device capable of implementing the image processing method, and the electronic device may be a processor of an intelligent image collector, a local processor independent of the image collector, or a cloud processor. Referring to fig. 1, the method of the embodiment of the present application specifically includes:
s110, determining an initial histogram according to a high-pass filtering image of an image to be processed and the image to be processed, and determining a detail histogram of the image to be processed according to the initial histogram.
The image to be processed may be an image acquired by an image acquirer, or an image acquired from a local area or the internet, the format and bit width of the image to be processed are not specifically limited, and the image to be processed may be an image to be processed in any format and bit width, and may be processed by the method in the embodiment of the present application. The high-pass filtering algorithm is not limited, and may be unsharp mask filtering, Sobel operator filtering, DOG operator filtering, LOG operator filtering, Laplacian operator filtering, or the like. The size of the filter kernel is not limited in the embodiments of the present application, and may be, for example, 3 × 3, 5 × 5, or 7 × 7, and the filter kernel may be in the form of
Treating the processing graph by using a filter kernelBefore processing the image, the image to be processed is subjected to edge expansion processing according to the size of the filtering kernel, so that the pixel points at the edge can be positioned in the center of the filtering kernel for convolution processing.
The initial histogram is directly determined according to a high-pass filtering image of the image to be processed and the image to be processed, the abscissa value of the initial histogram can be the gray level in the image to be processed, and the ordinate value of the initial histogram can be the accumulated value of detail data of pixel points corresponding to the gray level in the high-pass filtering image. And further processing the initial histogram to obtain a detail histogram. The initial histogram may be processed, for example, by filtering out part of data in the initial histogram, adjusting the order of data in the initial histogram, and the like, and may be processed according to actual requirements.
S120, determining a first platform threshold value and a second platform threshold value from the statistics values of the detail cumulative histogram according to the relationship between a first reference threshold value and the statistics values of the detail cumulative histogram and the relationship between a second reference threshold value and the statistics values of the detail cumulative histogram; wherein the first reference threshold and the second reference threshold are determined according to a maximum statistic of the cumulative histogram of details. Wherein the cumulative histogram of details is a cumulative histogram of the histogram of details;
the statistical value of the detail cumulative histogram may be a statistical value obtained by accumulating the sum of the detail data of the detail histogram. The first reference threshold and the second reference threshold may be determined according to a maximum statistical value of the detail cumulative histogram, for example, the first reference threshold and the second reference threshold may be calculated according to the following formulas:
histSumUp＝histSum*t
histSumDown＝histSum*(1-t)
the histSumDown may be a first reference threshold, the hissumup may be a second reference threshold, and the histSum may be a maximum statistical value of the detail cumulative histogram, where 0< t <1, and t may be selected according to an actual situation, for example, may be selected to be 0.85. In different scenarios, t may take different values.
The detail cumulative histogram calculation formula is:
accSortHist(g)＝accSortHist(g-1)+sortHist(g)
wherein accSortHist (g) is a detail cumulative histogram, sorHist (g) is a detail histogram, and g is a gray level.
Illustratively, the first and second plateau thresholds are determined in the statistics of the cumulative histogram of detail with reference to the first and second reference thresholds. For example, of the statistics of the cumulative detail histogram, the statistics closest to and greater than the first reference threshold value may be used as the first plateau threshold value, the statistics closest to and greater than the second reference threshold value may be used as the second plateau threshold value, or the statistics closest to and less than the second reference threshold value may be used as the first plateau threshold value, or the statistics closest to and greater than the first reference threshold value may be used as the second plateau threshold value, or the statistics closest to and less than the second reference threshold value may be used as the first plateau threshold value, or the statistics closest to and less than the first reference threshold value may be used as the second plateau threshold value, or the statistics closest to and less than the first reference threshold value may be used as the first plateau threshold value, and taking the statistic value which is closest to the second reference threshold value and is larger than the second reference threshold value as a second platform threshold value.
The method has the advantages that the detail accumulation histogram comprises the detail information of the image to be processed, and the first platform threshold value and the second platform threshold value are determined according to the detail accumulation histogram, so that the first platform threshold value and the second platform threshold value are suitable for processing the image to be processed under the current scene, and the image processing effect is improved.
S130, determining a dual-platform histogram according to the first platform threshold value, the second platform threshold value and the initial histogram.
Illustratively, a dual-plateau histogram is constructed based on the relationship between the first plateau threshold and the statistics of the initial histogram, and the relationship between the second plateau threshold and the statistics of the initial histogram. Specifically, for example, if the first plateau threshold is smaller than the second plateau threshold, the relationship between the statistical value of the initial histogram and the first plateau threshold and the second plateau threshold is determined. And if the statistic value of the initial histogram is greater than or equal to the first platform threshold value and less than or equal to the second platform threshold value, the statistic value of the initial histogram is kept, so that the dual-platform histogram is formed.
S140, determining a gray level mapping relation according to the double-platform cumulative histogram, and processing the image to be processed based on the gray level mapping relation to obtain a target image. Wherein the dual-plateau cumulative histogram is a cumulative histogram of the dual-plateau histogram.
Exemplarily, a gray mapping relation is determined according to a double-platform cumulative histogram, so that a numerical value corresponding to each gray level is determined, a numerical value corresponding to each pixel point in the image to be processed is determined according to the gray level of the pixel point, and the pixel point is given to obtain the target image.
The gray mapping relationship may be determined according to an actual situation, for example, the gray mapping relationship is determined according to a statistical value corresponding to each gray level in the dual-platform cumulative histogram, a statistical value corresponding to the maximum gray level, and a gray mapping range value.
In the embodiment of the application, the initial histogram is determined according to the high-pass filtering image of the image to be processed and the image to be processed, and determining a detail histogram of the image to be processed according to the initial histogram, thereby counting high-frequency components of the image to be processed, determining a detail part of the image to be processed, determining a first plateau threshold value and a second plateau threshold value from the statistics of the cumulative detail histogram based on a relationship of a first reference threshold value to the statistics of the cumulative detail histogram and a relationship of a second reference threshold value to the statistics of the cumulative detail histogram, thereby, the detailed part is taken into consideration in the process of determining the first platform threshold and the second platform threshold so as to be suitable for the image to be processed under the current scene, determining a dual-platform histogram according to the first platform threshold, the second platform threshold and the initial histogram; and determining a gray level mapping relation according to the double-platform cumulative histogram, and processing the image to be processed based on the gray level mapping relation to obtain a target image, so that the processing effect of the image to be processed is improved.
Fig. 2 is a flowchart of an image processing method according to another embodiment of the present application. In the embodiment of the application, for further optimization of the embodiment, S110 is refined into S210-S240. Details which are not described in detail in the examples of the present application are described in the above examples. Referring to fig. 2, an image processing method provided in an embodiment of the present application may include:
s210, traversing pixel points of the image to be processed, and taking the pixel points of the image to be processed with the same gray level as target pixel points.
Illustratively, traversal is performed from the first pixel point at the upper left corner of the image to be processed, and the gray level of each pixel point is determined. And taking the pixel points with the same pixel level as target pixel points. And for the gray levels of the target pixel points which are not zero, each gray level corresponds to a group of target pixel points, and the number of the group of pixel points is at least one.
S220, determining the initial histogram according to the sum of the detail data corresponding to the target pixel point in the high-pass filtering image and the corresponding gray level.
Illustratively, for each gray level, determining the detail data corresponding to the corresponding target pixel point in the high-pass filtered image, taking the sum of the detail data corresponding to a group of target pixel points corresponding to each gray level in the high-pass filtered image as the detail data statistic value corresponding to the gray level, taking the gray level as an abscissa value, and taking the detail data statistic value corresponding to the gray level as an ordinate value, and determining the initial histogram. In the embodiment of the present application, the gray level of the image to be processed is from 0, and if it is desired that the gray level in the histogram starts from 1, each gray level may be incremented by one, and the corresponding detail data statistic value is not changed, so that the abscissa value starts from 1.
Wherein the detail data may be imgHpss (i, j)αThe imgHpss (i, j) is a value corresponding to the pixel point (i, j) in the high-pass filtering image, alpha is a detail degree, and can be selected according to actual conditions, if the detail is desired to be emphasized, the influence of the detail is increased, alpha can be set to be a larger value, if the detail is not desired to be emphasized, the influence of the detail is reduced, alpha can be set to be a smaller value, and alpha is larger than or equal to 0.
And S230, removing an item with a vertical coordinate value of zero in the initial histogram, and compressing a horizontal coordinate value of the initial histogram to obtain a filtered histogram.
In the embodiment of the application, the initial histogram is determined according to the sum of the detail data corresponding to the target pixel point in the high-pass filtered image and the corresponding gray level, and the default case is that the sum of the detail data of the target pixel point is used as the ordinate, and the corresponding gray level is used as the abscissa, so that the operations on the abscissa and the ordinate in S230-S240 are performed. For example, if the ordinate value in the initial histogram is zero, it indicates that the pixel point range corresponding to the gray level does not include the detail information, and the ordinate value corresponding to the gray level may be removed to simplify the initial histogram. Since the ordinate values are removed and the corresponding abscissa values are removed, the abscissa values are reduced, and the abscissa values can be compressed to remain at equally spaced points in order to distribute the abscissa values at equally spaced intervals. For example, if the initial histogram has 256 abscissa values of 1-256 and an interval of 1, and 10 items with zero ordinate value are removed, the corresponding abscissa value is also removed, and 246 abscissa values remain, the abscissa values are compressed to 1-246, so that the abscissa values are equally spaced. And forming a filtering histogram by the compressed abscissa values and the ordinate values which are left after removing the items with the ordinate values being zero, wherein the abscissa values and the ordinate values are in one-to-one correspondence in sequence.
For example, the initial histogram is determined according to the sum of the detail data corresponding to the target pixel point in the high-pass filtered image and the corresponding gray level, in an actual case, the sum of the detail data corresponding to the target pixel point may be used as an abscissa, and the corresponding gray level is used as an ordinate, when the operations of S230 to S240 are subsequently performed, the operation for the ordinate is modified into the operation for the abscissa, and the operation for the abscissa is modified into the operation for the ordinate, that is, "removing an item whose abscissa value is zero in the initial histogram, and compressing the ordinate of the initial histogram to obtain the filtered histogram. And arranging the abscissa values of the filtering histogram according to the size sequence to obtain a detail histogram ".
S240, arranging the ordinate values of the filtering histogram according to the size sequence to obtain a detail histogram.
For example, for the filtered histogram, the abscissa values are unchanged, and the ordinate values are arranged in order of magnitude to obtain a detail histogram. The sorted detail histogram can more intuitively show the change situation of the vertical coordinate value, so that the first platform threshold value and the second platform threshold value can be conveniently and intuitively determined according to the detail histogram subsequently and quickly.
S250, determining a first platform threshold value and a second platform threshold value from the statistics values of the detail cumulative histogram according to the relationship between a first reference threshold value and the statistics values of the detail cumulative histogram and the relationship between a second reference threshold value and the statistics values of the detail cumulative histogram; wherein the first reference threshold and the second reference threshold are determined according to a maximum statistic of the cumulative histogram of details.
S260, determining a dual-platform histogram according to the first platform threshold value, the second platform threshold value and the initial histogram.
S270, determining a gray level mapping relation according to the double-platform cumulative histogram, and processing the image to be processed based on the gray level mapping relation to obtain a target image.
In the embodiment of the application, the initial histogram is determined according to the sum of the detail data corresponding to the target pixel point in the high-pass filtering image and the corresponding gray level, the item with the vertical coordinate value of zero in the initial histogram is removed, the horizontal coordinate value of the initial histogram is compressed to obtain the filtering histogram, the vertical coordinate value of the filtering histogram is arranged according to the size sequence to obtain the detail histogram, so that the high-frequency detail information in the image to be processed is counted, a double-platform threshold value is determined according to the detail information subsequently, and the scene applicability of the image processing method is improved.
Fig. 3 is a flowchart of an image processing method according to another embodiment of the present application. In order to further optimize the embodiments, the embodiment of the present application details S120 to S320, and S140 to S350 to S370, when it needs to be explained, the refinement of S120 and the refinement of S140 may not be performed simultaneously, do not affect each other, may be performed only on one step, or may perform the refinement of both S120 and S140, and details described in the embodiment of the present application details the embodiments described above. Referring to fig. 3, an image processing method provided in an embodiment of the present application may include:
s310, determining an initial histogram according to a high-pass filtering image of an image to be processed and the image to be processed, and determining a detail histogram of the image to be processed according to the initial histogram.
S320, accumulating the detail which is larger than the first reference threshold and has the smallest difference with the first reference threshold into the statistic of the histogram as a first platform threshold; and accumulating the detail which is larger than the second reference threshold and has the smallest difference with the second reference threshold as the statistic of the histogram as a second platform threshold.
Illustratively, in the detail cumulative histogram, if there is a statistical value of the detail cumulative histogram that is greater than a first reference threshold value and a difference from the first reference threshold value is smallest, the statistical value of the detail cumulative histogram is taken as a first plateau threshold value, and if there is a statistical value of the detail cumulative histogram that is greater than a second reference threshold value and a difference from the second reference threshold value is smallest, the statistical value is taken as a second plateau threshold value.
S330, determining a first gray level corresponding to the first platform threshold value and a second gray level corresponding to the second platform threshold value in the detail accumulation histogram.
Illustratively, a first gray level corresponding to a first plateau threshold value and a second gray level corresponding to a second plateau threshold value are recorded in the detail accumulation histogram. The first gray level is less than the second gray level if the first plateau threshold is less than the second plateau threshold.
S340, determining a dual-platform histogram according to the first platform threshold, the second platform threshold and the initial histogram.
In an embodiment of the present application, the first plateau threshold is smaller than the second plateau threshold; correspondingly, determining a dual-plateau histogram according to the first plateau threshold, the second plateau threshold, and the initial histogram, including: if the statistic value of the initial histogram is smaller than the first platform threshold value, replacing the statistic value of the initial histogram with the first platform threshold value; if the statistic value of the initial histogram is larger than the second platform threshold value, replacing the statistic value of the initial histogram with the second platform threshold value; if the statistic value of the initial histogram is larger than or equal to the first platform threshold value and smaller than or equal to the second platform threshold value, the statistic value of the initial histogram is reserved.
For example, if t >0.5, the first reference threshold is smaller than the second reference threshold, the first plateau threshold is smaller than the second plateau threshold, and the first gray scale is smaller than the second gray scale. Determining a dual-plateau histogram according to the first plateau threshold, the second plateau threshold, and the initial histogram, for example, determining the dual-plateau histogram according to the following formula:
wherein dphist (g) is a statistical value of the dual-plateau histogram, hist (g) is a statistical value of the initial histogram, TDown is a first plateau threshold, TUp is a second plateau threshold, and g is a gray level.
S350, determining a statistic value ratio according to the statistic value of the double-platform cumulative histogram corresponding to each gray level and the statistic value of the double-platform cumulative histogram corresponding to the maximum gray level.
For example, determining a dual-plateau cumulative histogram from a dual-plateau cumulative histogram may be determined according to the following formula:
accDPHist(g)＝accDPHist(g-1)+DPHist(g)
wherein accdphist (g) is a statistical value of a two-stage cumulative histogram, and dphist (g) is a statistical value of a two-stage histogram.
Illustratively, the statistical value ratio may be determined according to the following formula:
wherein, P is a statistic value proportion, accdphist (g) is a statistic value of the dual-stage cumulative histogram, accdphist (m) is a statistic value of the dual-stage cumulative histogram corresponding to the maximum gray level, and m is the maximum gray level in the dual-stage cumulative histogram.
And S360, determining the gray mapping relation according to the product of the statistic value proportion and the gray mapping range value.
For example, the gray scale mapping relationship may be expressed as:
HistB(g)＝P*R
wherein HistB (g) is a gray mapping relation, R is a gray mapping range value, and P is a statistic value proportion.
In an embodiment of the present application, the determining process of the grayscale mapping range value includes: determining the maximum gray level of the target image according to the preset bit width of the target image; if the absolute value of the difference between the first gray level and the second gray level is less than or equal to the maximum gray level, the gray mapping range value is the absolute value of the difference between the first gray level and the second gray level; and if the absolute value of the difference between the first gray level and the second gray level is greater than the maximum gray level, the gray mapping range value is the maximum gray level.
Illustratively, the grayscale mapping range value is determined according to the following formula:
wherein, R is a gray mapping range value, rangeDown is a first gray level, rangeUp is a second gray level, and n is a maximum gray level determined according to a preset bit width of the target image.
And S370, processing the image to be processed based on the gray mapping relation to obtain a target image. Illustratively, the image to be processed is processed based on the following formula:
imgOut(i,j)＝HistB(imgIn(i,j)+1)
wherein imgOut (i, j) is a target image, HistB (imgIn (i, j) +1) is a gray level mapping relationship, imgIn (i, j) is a gray level corresponding to a pixel point (i, j) of an image to be processed, and since the gray level of the histogram in the embodiment of the present application is counted from 1, one is added on the basis of the gray level of the image to be processed, so as to correspond to the gray level in the histogram.
In the embodiment of the application, the first platform threshold value and the second platform threshold value are determined based on the detail information of the image to be processed, the double-platform histogram is further determined, and the image to be processed is processed, so that the processing effect of the image to be processed in different scenes is improved.
Fig. 4 is a schematic structural diagram of an image processing apparatus according to an embodiment of the present application. The device is applicable to the case of processing images. Typically, the embodiment of the application is suitable for the situation of enhancing the image to be processed based on the dual-platform histogram. The apparatus may be implemented by software and/or hardware, and the apparatus may be integrated in an electronic device. Referring to fig. 4, the apparatus specifically includes:
a detail histogram determining module 410, configured to determine an initial histogram according to a high-pass filtered image of an image to be processed and the image to be processed, and determine a detail histogram of the image to be processed according to the initial histogram;
a threshold determination module 420, configured to determine a first plateau threshold and a second plateau threshold from the statistics of the cumulative detail histogram according to a relationship between a first reference threshold and the statistics of the cumulative detail histogram and a relationship between a second reference threshold and the statistics of the cumulative detail histogram; wherein the first reference threshold and the second reference threshold are determined according to a maximum statistical value of the cumulative detail histogram, the cumulative detail histogram being a cumulative histogram of the detail histogram;
a dual-platform histogram determining module 430, configured to determine a dual-platform histogram according to the first platform threshold, the second platform threshold, and the initial histogram;
the processing module 440 is configured to determine a grayscale mapping relationship according to the dual-platform cumulative histogram, and process the image to be processed based on the grayscale mapping relationship to obtain a target image; wherein the dual-plateau cumulative histogram is a cumulative histogram of the dual-plateau histogram.
In this embodiment of the application, the detail histogram determining module 410 includes:
the target pixel point determining unit is used for traversing pixel points of the image to be processed and taking the pixel points of the image to be processed with the same gray level as target pixel points;
and the initial histogram determining unit is used for determining the initial histogram according to the sum of the detail data corresponding to the target pixel point in the high-pass filtering image and the corresponding gray level.
In this embodiment of the application, the detail histogram determining module 410 includes:
the filtering histogram determining unit is used for removing an item of which the ordinate value is zero in the initial histogram and compressing the abscissa value of the initial histogram to obtain a filtering histogram;
and the sorting unit is used for sorting the ordinate values of the filtering histogram according to the magnitude sequence to obtain a detail histogram.
In this embodiment, the threshold determining module 420 includes:
a first plateau threshold value determining unit configured to accumulate, as a first plateau threshold value, a statistical value of a detail cumulative histogram that is larger than the first reference threshold value and has a smallest difference from the first reference threshold value; and the number of the first and second groups,
a second plateau threshold determining unit configured to accumulate, as a second plateau threshold, a statistical value of a detail cumulative histogram that is larger than the second reference threshold and has a smallest difference from the second reference threshold;
correspondingly, the device further comprises:
and the gray level determining unit is used for determining a first gray level corresponding to the first platform threshold value and a second gray level corresponding to the second platform threshold value in the detail accumulation histogram.
In an embodiment of the present application, the first plateau threshold is smaller than the second plateau threshold;
accordingly, the dual-platform histogram determination module 430 includes:
a first comparing unit, configured to replace the statistics value of the initial histogram with the first plateau threshold value if the statistics value of the initial histogram is smaller than the first plateau threshold value;
a second comparing unit, configured to replace the statistics value of the initial histogram with the second bin threshold if the statistics value of the initial histogram is greater than the second bin threshold;
a third comparing unit, configured to keep the statistics of the initial histogram if the statistics of the initial histogram is greater than or equal to the first plateau threshold and is less than or equal to the second plateau threshold.
In this embodiment, the processing module 440 includes:
the statistic proportion value determining unit is used for determining the statistic proportion according to the statistic of the double-platform cumulative histogram corresponding to each gray level and the statistic of the double-platform cumulative histogram corresponding to the maximum gray level;
and the gray mapping relation determining unit is used for determining the gray mapping relation according to the product of the statistic value proportion and the gray mapping range value.
In an embodiment of the present application, the apparatus further includes:
the maximum gray level determining module is used for determining the maximum gray level of the target image according to the preset bit width of the target image;
a first range value determining module, configured to determine that the grayscale mapping range value is an absolute value of a difference between the first grayscale and the second grayscale if the absolute value of the difference between the first grayscale and the second grayscale is less than or equal to the maximum grayscale;
a second range value determining module, configured to determine that the grayscale mapping range value is the maximum grayscale level if an absolute value of a difference between the first grayscale level and the second grayscale level is greater than the maximum grayscale level.
The image processing device provided by the embodiment of the application can execute the image processing method provided by any embodiment of the application, and has corresponding functional modules and beneficial effects of the execution method.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application. FIG. 5 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. 5 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. 5, 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 implement the image processing method provided in the embodiment of the present application, including:
determining an initial histogram according to a high-pass filtering image of an image to be processed and the image to be processed, and determining a detail histogram of the image to be processed according to the initial histogram;
determining a first platform threshold value and a second platform threshold value from the statistics values of the detail cumulative histogram according to the relation between a first reference threshold value and the statistics values of the detail cumulative histogram and the relation between a second reference threshold value and the statistics values of the detail cumulative histogram; wherein the first reference threshold and the second reference threshold are determined according to a maximum statistical value of the cumulative detail histogram, the cumulative detail histogram being a cumulative histogram of the detail histogram;
determining a dual-platform histogram according to the first platform threshold, the second platform threshold and the initial histogram;
determining a gray level mapping relation according to the double-platform cumulative histogram, and processing the image to be processed based on the gray level mapping relation to obtain a target image; wherein the dual-plateau cumulative histogram is a cumulative histogram of the dual-plateau histogram.
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. 5, and commonly referred to as a "hard drive"). Although not shown in FIG. 5, 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 such cases, each drive may be connected to bus 518 through 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 application.
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 embodiments described herein.
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) via the network adapter 520. As shown in FIG. 5, 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. 5, 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 stored in the memory 528, for example, to implement an image processing method provided in the embodiment of the present application.
One embodiment of the present application provides a storage medium containing computer-executable instructions that, when executed by a computer processor, perform an image processing method, comprising:
determining an initial histogram according to a high-pass filtering image of an image to be processed and the image to be processed, and determining a detail histogram of the image to be processed according to the initial histogram;
determining a first platform threshold value and a second platform threshold value from the statistics values of the detail cumulative histogram according to the relation between a first reference threshold value and the statistics values of the detail cumulative histogram and the relation between a second reference threshold value and the statistics values of the detail cumulative histogram; wherein the first reference threshold and the second reference threshold are determined according to a maximum statistical value of the cumulative detail histogram, the cumulative detail histogram being a cumulative histogram of the detail histogram;
determining a dual-platform histogram according to the first platform threshold, the second platform threshold and the initial histogram;
determining a gray level mapping relation according to the double-platform cumulative histogram, and processing the image to be processed based on the gray level mapping relation to obtain a target image; wherein the dual-plateau cumulative histogram is a cumulative histogram of the dual-plateau histogram.
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 application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, 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 application and the technical principles employed. It will be understood by those skilled in the art that the present application 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 application. Therefore, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the appended claims.
Claims (10)
1. An image processing method, characterized in that the method comprises:
determining an initial histogram according to a high-pass filtering image of an image to be processed and the image to be processed, and determining a detail histogram of the image to be processed according to the initial histogram;
determining a first platform threshold value and a second platform threshold value from the statistics values of the detail cumulative histogram according to the relation between a first reference threshold value and the statistics values of the detail cumulative histogram and the relation between a second reference threshold value and the statistics values of the detail cumulative histogram; wherein the first reference threshold and the second reference threshold are determined according to a maximum statistical value of the cumulative detail histogram, the cumulative detail histogram being a cumulative histogram of the detail histogram;
determining a dual-platform histogram according to the first platform threshold, the second platform threshold and the initial histogram;
determining a gray level mapping relation according to the double-platform cumulative histogram, and processing the image to be processed based on the gray level mapping relation to obtain a target image; wherein the dual-plateau cumulative histogram is a cumulative histogram of the dual-plateau histogram.
2. The method of claim 1, wherein determining an initial histogram from a high-pass filtered image of an image to be processed and the image to be processed comprises:
traversing pixel points of the image to be processed, and taking the pixel points of the image to be processed with the same gray level as target pixel points;
and determining the initial histogram according to the sum of the detail data corresponding to the target pixel point in the high-pass filtering image and the corresponding gray level.
3. The method of claim 2, wherein determining a detail histogram for the image to be processed from the initial histogram comprises:
removing items with vertical coordinate values of zero in the initial histogram, and compressing horizontal coordinate values of the initial histogram to obtain a filtered histogram;
and arranging the ordinate values of the filtering histogram according to the magnitude sequence to obtain a detail histogram.
4. The method according to any one of claims 1-3, wherein determining a first and a second threshold from the statistics of the cumulative detail histogram according to a relation of a first reference threshold to the statistics of the cumulative detail histogram and a relation of a second reference threshold to the statistics of the cumulative detail histogram comprises:
accumulating the detail which is larger than the first reference threshold and has the smallest difference value with the first reference threshold as a statistic value of a histogram as a first platform threshold; and the number of the first and second groups,
accumulating the statistics of the detail cumulative histogram which is larger than the second reference threshold and has the smallest difference with the second reference threshold as a second platform threshold;
accordingly, the method further comprises:
and determining a first gray level corresponding to the first platform threshold value and a second gray level corresponding to the second platform threshold value in the detail accumulation histogram.
5. The method of any of claims 1-3, wherein the first plateau threshold is less than the second plateau threshold;
correspondingly, determining a dual-plateau histogram according to the first plateau threshold, the second plateau threshold, and the initial histogram, including:
if the statistic value of the initial histogram is smaller than the first platform threshold value, replacing the statistic value of the initial histogram with the first platform threshold value;
if the statistic value of the initial histogram is larger than the second platform threshold value, replacing the statistic value of the initial histogram with the second platform threshold value;
if the statistic value of the initial histogram is larger than or equal to the first platform threshold value and smaller than or equal to the second platform threshold value, the statistic value of the initial histogram is reserved.
6. The method of claim 4, wherein determining a grayscale mapping from the dual-plateau cumulative histogram comprises:
determining a statistic value ratio according to the statistic value of the double-platform cumulative histogram corresponding to each gray level and the statistic value of the double-platform cumulative histogram corresponding to the maximum gray level;
and determining the gray mapping relation according to the product of the statistic value proportion and the gray mapping range value.
7. The method of claim 6, wherein the determining of the grayscale mapping range value comprises:
determining the maximum gray level of the target image according to the preset bit width of the target image;
if the absolute value of the difference between the first gray level and the second gray level is less than or equal to the maximum gray level, the gray mapping range value is the absolute value of the difference between the first gray level and the second gray level;
and if the absolute value of the difference between the first gray level and the second gray level is greater than the maximum gray level, the gray mapping range value is the maximum gray level.
8. An image processing apparatus, characterized in that the apparatus comprises:
the detail histogram determining module is used for determining an initial histogram according to a high-pass filtering image of an image to be processed and the image to be processed, and determining a detail histogram of the image to be processed according to the initial histogram;
a threshold determination module, configured to determine a first plateau threshold and a second plateau threshold from the statistics of the cumulative detail histogram according to a relationship between a first reference threshold and the statistics of the cumulative detail histogram and a relationship between a second reference threshold and the statistics of the cumulative detail histogram; wherein the first reference threshold and the second reference threshold are determined according to a maximum statistical value of the cumulative detail histogram, the cumulative detail histogram being a cumulative histogram of the detail histogram;
a dual-platform histogram determination module, configured to determine a dual-platform histogram according to the first platform threshold, the second platform threshold, and the initial histogram;
the processing module is used for determining a gray mapping relation according to the double-platform cumulative histogram and processing the image to be processed based on the gray mapping relation to obtain a target image; wherein the dual-plateau cumulative histogram is a cumulative histogram of the dual-plateau histogram.
9. 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 processing method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the image processing method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110590447.9A CN113191990B (en) | 2021-05-28 | 2021-05-28 | Image processing method, device, electronic equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110590447.9A CN113191990B (en) | 2021-05-28 | 2021-05-28 | Image processing method, device, electronic equipment and medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113191990A true CN113191990A (en) | 2021-07-30 |
| CN113191990B CN113191990B (en) | 2023-05-23 |
Family
ID=76985936
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110590447.9A Active CN113191990B (en) | 2021-05-28 | 2021-05-28 | Image processing method, device, electronic equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113191990B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115660997A (en) * | 2022-11-08 | 2023-01-31 | 杭州微影软件有限公司 | Image data processing method and device and electronic equipment |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102456224A (en) * | 2010-10-19 | 2012-05-16 | 高森 | Real-time digital image enhancement method based on field programmable gate array (FPGA) |
| CN102521813A (en) * | 2011-11-21 | 2012-06-27 | 华中科技大学 | Infrared image adaptive enhancement method based on dual-platform histogram |
| CN103353349A (en) * | 2013-06-18 | 2013-10-16 | 南京理工大学 | Infrared-thermometer self-adaption three platform histogram equalization system and method thereof |
| CN103778900A (en) * | 2012-10-23 | 2014-05-07 | 浙江大华技术股份有限公司 | Image processing method and system |
| CN106097286A (en) * | 2016-06-21 | 2016-11-09 | 浙江大华技术股份有限公司 | A kind of method and device of image procossing |
| CN107292856A (en) * | 2017-06-12 | 2017-10-24 | 北京理工大学 | A kind of method of infrared focal plane detector image enhaucament |
| US20180349724A1 (en) * | 2017-05-31 | 2018-12-06 | Shanghai United Imaging Healthcare Co., Ltd. | Method and system for image processing |
| CN109377464A (en) * | 2018-10-08 | 2019-02-22 | 嘉应学院 | A kind of Double plateaus histogram equalization method and its application system of infrared image |
| US20190392311A1 (en) * | 2018-06-21 | 2019-12-26 | Deep Force Ltd. | Method for quantizing a histogram of an image, method for training a neural network and neural network training system |
| CN111784609A (en) * | 2020-07-02 | 2020-10-16 | 烟台艾睿光电科技有限公司 | Image dynamic range compression method and device and computer readable storage medium |
| US20200380651A1 (en) * | 2019-05-28 | 2020-12-03 | Seek Thermal, Inc. | Adaptive gain adjustment for histogram equalization in an imaging system |
| CN112348763A (en) * | 2020-11-09 | 2021-02-09 | 西安宇视信息科技有限公司 | Image enhancement method, device, electronic equipment and medium |
-
2021
- 2021-05-28 CN CN202110590447.9A patent/CN113191990B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102456224A (en) * | 2010-10-19 | 2012-05-16 | 高森 | Real-time digital image enhancement method based on field programmable gate array (FPGA) |
| CN102521813A (en) * | 2011-11-21 | 2012-06-27 | 华中科技大学 | Infrared image adaptive enhancement method based on dual-platform histogram |
| CN103778900A (en) * | 2012-10-23 | 2014-05-07 | 浙江大华技术股份有限公司 | Image processing method and system |
| CN103353349A (en) * | 2013-06-18 | 2013-10-16 | 南京理工大学 | Infrared-thermometer self-adaption three platform histogram equalization system and method thereof |
| CN106097286A (en) * | 2016-06-21 | 2016-11-09 | 浙江大华技术股份有限公司 | A kind of method and device of image procossing |
| US20180349724A1 (en) * | 2017-05-31 | 2018-12-06 | Shanghai United Imaging Healthcare Co., Ltd. | Method and system for image processing |
| CN107292856A (en) * | 2017-06-12 | 2017-10-24 | 北京理工大学 | A kind of method of infrared focal plane detector image enhaucament |
| US20190392311A1 (en) * | 2018-06-21 | 2019-12-26 | Deep Force Ltd. | Method for quantizing a histogram of an image, method for training a neural network and neural network training system |
| CN109377464A (en) * | 2018-10-08 | 2019-02-22 | 嘉应学院 | A kind of Double plateaus histogram equalization method and its application system of infrared image |
| US20200380651A1 (en) * | 2019-05-28 | 2020-12-03 | Seek Thermal, Inc. | Adaptive gain adjustment for histogram equalization in an imaging system |
| CN111784609A (en) * | 2020-07-02 | 2020-10-16 | 烟台艾睿光电科技有限公司 | Image dynamic range compression method and device and computer readable storage medium |
| CN112348763A (en) * | 2020-11-09 | 2021-02-09 | 西安宇视信息科技有限公司 | Image enhancement method, device, electronic equipment and medium |
Non-Patent Citations (2)
| Title |
|---|
| 宋岩峰，等: "基于双平台直方图的红外图像增强算法", 红外与激光工程 * |
| 毛义伟，等: "基于改进型平台直方图的红外均衡化算法", 光学与光电技术 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115660997A (en) * | 2022-11-08 | 2023-01-31 | 杭州微影软件有限公司 | Image data processing method and device and electronic equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113191990B (en) | 2023-05-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108876733B (en) | Image enhancement method, device, equipment and storage medium | |
| CN109146816B (en) | Image filtering method and device, electronic equipment and storage medium | |
| CN111950723A (en) | Neural network model training method, image processing method, device and terminal equipment | |
| CN114169381A (en) | Image annotation method and device, terminal equipment and storage medium | |
| Singh et al. | Contrast enhancement and brightness preservation using global-local image enhancement techniques | |
| CN110796624B (en) | Image generation method and device and electronic equipment | |
| CN111861938B (en) | Image denoising method and device, electronic equipment and readable storage medium | |
| CN112348763A (en) | Image enhancement method, device, electronic equipment and medium | |
| WO2022000861A1 (en) | Image tampering area locating method and apparatus, and storage medium | |
| CN115293993B (en) | Sampling method and system for filtering salt and pepper noise | |
| US10964028B2 (en) | Electronic device and method for segmenting image | |
| CN111383178A (en) | Image enhancement method and device and terminal equipment | |
| CN113344801A (en) | Image enhancement method, system, terminal and storage medium applied to gas metering facility environment | |
| CN114298985B (en) | Defect detection method, device, equipment and storage medium | |
| CN113191990B (en) | Image processing method, device, electronic equipment and medium | |
| CN114943649A (en) | Image deblurring method, device and computer readable storage medium | |
| CN110751156A (en) | Method, system, device and medium for table line bulk interference removal | |
| CN114862897A (en) | Image background processing method and device and electronic equipment | |
| CN111311610A (en) | Image segmentation method and terminal equipment | |
| CN113762027B (en) | Abnormal behavior identification method, device, equipment and storage medium | |
| CN113163101B (en) | Image exposure adjusting method, device, equipment and medium | |
| CN114596210A (en) | Noise estimation method, device, terminal equipment and computer readable storage medium | |
| CN110264488B (en) | Binary image edge extraction device | |
| CN113628192A (en) | Image blur detection method, device, apparatus, storage medium, and program product | |
| KR101462421B1 (en) | Method and apparatus of histogram equalizer for contrast enhancement of image using histogram compression |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |