CN115018714A - Image pixel value mapping method, imaging device and computer storage medium - Google Patents
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Abstract
The invention belongs to the technical field of image sensors, and relates to an image pixel value mapping method, which comprises the steps of dividing pixel points of a brightness adjusting image into low-brightness points, medium-brightness points and high-brightness points by calculating a brightness threshold; and mapping the pixel value of each pixel point of the brightness adjusting image into the range of [0,2^ n-1] according to the pixel value of the pixel point to be mapped and the ratio and the mapping value of the low, medium and high brightness points in the set neighborhood of the pixel point to be mapped to obtain a pixel value mapping image. The invention can automatically complete the task of image pixel value mapping without manual intervention, is suitable for gray level images and color images, and is suitable for image pixel value mapping of continuous video images. The invention also provides an imaging device and a computer storage medium.
Description
Technical Field
The present invention relates to the field of image sensor technologies, and in particular, to an image pixel value mapping method, an imaging device, and a computer storage medium.
Background
Image sensors are widely used in video surveillance and other related fields. In some traffic monitoring camera shooting application occasions, the brightness of a traffic signal lamp after being started is equivalent to the surrounding environment, and the brightness is brighter. When the image sensor is used for video monitoring to shoot a scene containing traffic lights, the red lights with weak illumination are prone to color cast (except red, other color channels are excessively exploded) in cloudy days, and the problem of traffic lights overexposure can occur in shooting at night. The reason for this problem is that the current image sensor is usually linear in design, and the linear image sensor has a small illumination range and cannot collect all signals from a low-illumination environment to a strong-light environment, so that the output dynamic range of the linear image sensor cannot simultaneously satisfy the brightness range of the traffic light and the surrounding environment. To solve the above problem, it is necessary to improve the dynamic range of the image output from the image sensor to meet the application requirements of different scenes.
The dynamic range of the output of the image sensor is increased by adopting a mode of outputting two frames of images for synthesis so as to increase the dynamic range. In the specific design, two frames of images have different exposure times, one frame of image has a long exposure time, and the other frame of image has a short exposure time. One frame of image with a long exposure time can obtain image details clearly in a low-illumination scene, and another frame of image with a short exposure time can obtain image details in a high-illumination scene. The two frames of images are combined, so that a clear image with details of both the low-illumination scene and the high-illumination scene can be obtained. However, in the implementation of the two-frame synthesis, the first frame image needs to be read and stored, and the second frame image needs to be read and then merged. Therefore, in a specific application, the two-frame image synthesis has a problem of motion blur.
Another kind of image sensor solution for this scene is to use different exposure times for different areas in one frame of exposure process, i.e. to control the exposure time so that the output one frame of image contains different exposures, thereby solving the problem of overexposure in the image. However, the existing image fusion method is to fuse complete scene images with different exposures, that is, to fuse multi-frame exposure images, and in the case of zone exposure, the different exposure images for fusion only contain a part of the scene, so the existing image fusion method cannot meet the requirement of zone exposure image fusion.
Therefore, in the image processing such as the image fusion processing described above, it is often necessary to map the pixel values of the image and then perform subsequent processing and application.
Disclosure of Invention
It is therefore an object of the present invention to provide an image pixel value mapping method, an imaging device and a computer storage medium for implementing various image processing applications.
The invention also provides an imaging device, which comprises a processor and a memory, wherein the memory is used for storing at least one instruction, and the processor is used for reading the at least one instruction and executing the method.
The present invention also provides a computer storage medium having at least one instruction stored therein, the at least one instruction being loaded and executed by a processor to implement the method described above.
The invention relates to an image pixel value mapping method, an imaging device and a computer storage medium, wherein pixel points of a brightness adjusting image are divided into low-brightness points, medium-brightness points and high-brightness points by calculating a brightness threshold; and mapping the pixel value of each pixel point of the brightness adjusting image into the range of [0,2^ n-1] according to the pixel value of the pixel point to be mapped and the ratio and the mapping value of the low, medium and high brightness points in the set neighborhood of the pixel point to be mapped to obtain a pixel value mapping image. The invention can automatically complete the task of image pixel value mapping without manual intervention, is suitable for gray level images and color images, and is suitable for image pixel value mapping of continuous video images. .
In order to make the aforementioned and other objects, features and advantages of the invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
FIG. 1 is a flowchart illustrating the steps of an image pixel value mapping method applied to an on-chip exposure image fusion method according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating an image pixel value mapping method applied to an image corresponding to each step of an on-chip exposure image fusion method according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating steps of a brightness adjustment method according to an embodiment of the invention;
FIG. 4 is a flowchart illustrating steps of a pixel value mapping method according to an embodiment of the invention;
FIG. 5 is a flowchart illustrating a method for maintaining color saturation according to an embodiment of the present invention;
FIG. 6 is a flowchart illustrating a transition processing method for a join area according to an embodiment of the present invention;
fig. 7 is a schematic diagram of a boundary pixel point in a transition processing method of a join region according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It is to be understood that the described embodiments are merely some embodiments of the invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
As shown in fig. 1 and fig. 2, an on-chip area exposure image fusion method according to the present invention includes the following steps.
Step S100: the method comprises the steps of carrying out brightness adjustment on an original image Src to be adjusted, dividing the original image Src to be adjusted into a plurality of exposure subareas, designating one exposure subarea as a reference subarea for primary adjustment, and adjusting the brightness of adjacent exposure subareas according to the reference subarea to enable the brightness of different exposure subareas to be consistent, so as to obtain a brightness adjustment image Src _ adj.
Step S200: pixel value mapping is carried out on each partition of the brightness adjustment image Src _ adj, and according to pixel values of a pixel point to be mapped and other pixel points in a set neighborhood where the pixel point is located, the pixel value of each pixel point of the brightness adjustment image Src _ adj is mapped into a range of [0,2^ n-1], so that a pixel value mapping image Src _ mapping of each partition is obtained. Wherein n is the bit width of the original image Src to be adjusted. In the embodiment of the present invention, n is 8, and thus the pixel value mapping range is [0,255 ]. Those skilled in the art will appreciate that n may take on other bit widths of 9, 10, 12, etc. in other embodiments.
Step S300: and carrying out color saturation maintenance on the original image Src to be adjusted, giving the pixel value of the high-brightness pixel point which is greater than the set brightness threshold value to the corresponding pixel point in the pixel value mapping image Src _ mapping, weighting the edge pixel point of the high-brightness pixel point which is assigned in the Src _ mapping image with the value of the pixel point in the set neighborhood, finishing transition processing, and obtaining a color saturation maintenance image Src _ ret.
Step S400: and performing linking area transition processing on adjacent exposure partitions, setting a linking transition area taking a linking boundary as a center in two adjacent exposure partitions, and adjusting the pixel value of a pixel point on one side of the linking boundary to be the weighted sum of the original pixel value and the pixel value of a boundary pixel point close to the linking boundary on the other side of the linking boundary in the linking transition area to complete the fusion of the partition exposure images.
In one embodiment, the color saturation maintaining in step S300 is not necessary, and the color saturation maintaining process may not be performed on the original image Src to be adjusted, and the transition process of the connected region in step S400 may be directly performed.
As shown in fig. 3, in one embodiment, in the brightness adjustment in step S100, the following steps are specifically included, with the middle partition of the original image Src to be adjusted as the reference partition for the first brightness adjustment.
Step S101: the middle partition of the Src image is selected as the reference partition.
Step S102: and judging whether Src is a gray image or not.
Step S103: and if the Src is a gray level image, assigning the Src image to the original gray level image Src _ gray to be adjusted, and entering step S105, otherwise, entering step S104.
Step S104: the Src image is converted into a gray-scale original image to be adjusted Src _ gray using the following formula (1):
Gray＝R*0.299+G*0.587+B*0.114 (1)
wherein, R, G and B are pixel values of R channel, G channel and B channel of the Src image respectively.
Step S105: the average brightness value ave _ ref of the reference partition of the Src _ gray image (in this embodiment, an overexposed pixel with a pixel value greater than 200 is not considered) and the average brightness value ave _ adj of the neighboring partition to be adjusted (in this embodiment, an overexposed pixel with a pixel value greater than 200 is not considered).
In the embodiment of the present invention, the average brightness value ave _ ref of the reference partition side of the original to-be-adjusted image Src _ gray near the N rows of the joining boundary and the average brightness value ave _ adj of the adjacent to-be-adjusted partition side near the N rows of the joining boundary are specifically counted.
When the average brightness value ave _ adj of the adjacent to-be-adjusted partitions is counted, firstly, the average value ave _ adj _ all of all pixel points in the N rows is calculated, then, the pixel points with the pixel values smaller than min { (2^ N-1) × A1 and ave _ adj _ all _ A2} in the N rows are considered, N is the bit width of the currently processed image, and finally, the average value of the points is calculated to obtain ave _ adj. Wherein min { (2^ n-1) × A1, ave _ adj _ all × A2} represents the smaller of these two values. In one embodiment, the setting parameter A1 is 0.9 and the setting parameter A2 is 1.5.
When the average brightness value ave _ ref of the reference partition is counted, firstly, the average value ave _ ref _ all of all the pixel points in the N rows is calculated, then the pixel points with the pixel values smaller than min { (2^ N-1) × B1 ave _ ref _ all { (B2) } in the N rows are considered, N is the bit width of the image currently processed, and finally the average value of the points is calculated to obtain ave _ ref. Wherein min { (2^ n-1) × B1, ave _ ref _ all × B2} represents taking the smaller of the two values. In one embodiment, the setting parameter B1 is 0.9, and the setting parameter B2 is 1.5.
The value range of N is [1, min { Height _ region _ adj, Height _ region _ ref } ], wherein Height _ region _ adj represents the Height of an adjacent partition to be adjusted, and Height _ region _ ref represents the Height of a reference partition. If the partition to be adjusted is close to the position of the linking boundary and has a highlight point, N takes a larger value, for example, more than 20 lines, in the value range [1, min { Height _ region _ adj, Height _ region _ ref } ] so as to weaken the influence of the highlight point on the average brightness; otherwise, N is within the above range [1, min { Height _ region _ adj, Height _ region _ ref } ] to take a smaller value, for example, within 10 rows, so as to reduce the amount of computation.
Step S106: and judging whether the difference value of ave _ adj and ave _ ref is smaller than a set first threshold value, wherein in one embodiment, the first threshold value is set to be 0.5, if | ave _ adj-ave _ ref | is smaller than 0.5, the brightness adjustment of the partition to be adjusted is completed, and the step S108 is executed, otherwise, the step S107 is executed.
Step S107: otherwise, calculating a ratio rate of the average brightness values of the reference partition and the partition to be adjusted, that is, rate ═ ave _ ref/ave _ adj, in an embodiment, it is defined that the ratio of the exposure gains of the respective partitions does not exceed 16, that is, the ratio rate is less than or equal to 16, and therefore, if the bit width of the reference partition is 8, the bit width of the pixel value of the partition to be adjusted is extended by 4 bits, that is, the bit width of the pixel value of the partition to be adjusted is 12, and then multiplying the pixel value of the partition to be adjusted of the Src image by the ratio, and entering step S102.
Step S108: and the Src image refers to the adjacent subareas of the subareas, and brightness adjustment is completed, and whether other unadjusted subareas exist is judged.
Step S109: if the brightness adjustment of the adjacent partition of the Src image reference partition is completed and there are other unadjusted partitions, the adjusted partition of the Src image with the adjacent unadjusted partition is used as the reference partition, and the adjacent unadjusted partition is used as the partition to be adjusted, and the process proceeds to step S102.
And if the brightness adjustment of all the subareas is finished, ending the brightness adjustment, and obtaining the brightness adjustment image Src _ adj with the consistent brightness of each subarea.
As shown in fig. 4, in one embodiment, in the pixel value mapping of step S200, calculating a brightness threshold divides pixel points of each partition of the brightness adjustment image Src _ adj into a low brightness point, a medium brightness point and a high brightness point; and according to pixel points to be mapped of each partition of the image Src _ adj and pixel values in a set neighborhood (for example, 9 x 9) of the pixel points, compressing a high dynamic range by using a logarithmic equation, and mapping the pixel values to a range of [0,2^ n-1], wherein the method comprises the following steps.
Step S201: carrying out gray level conversion on each partition of the brightness adjustment image Src _ adj by using a formula (1) to obtain a gray level brightness adjustment image Src _ adj _ gray of each partition of the image Src _ adj, wherein the gray level brightness adjustment image Src _ adj _ gray is used for representing the brightness of the image Src _ adj, and calculating a logarithmic average value lg _ ave of pixel values of the image Src _ adj _ gray by using a following formula (2), namely:
where Num is the total number of pixels in the image Src _ adj _ gray, and Src _ adj _ gray (x, y) represents the pixel value of the image Src _ adj _ gray in x rows and y columns, and in one embodiment, the setting parameter α is 0.0001.
Step S202: calculating a brightness threshold value Key, namely:
where, grayMax and grayMin denote the maximum pixel value and the minimum pixel value of the image Src _ adj _ gray, respectively.
Step S203: dividing the normalized Src _ adj _ gray image pixel points into low-brightness points, medium-brightness points and high-brightness points according to the brightness threshold Key:
L t ＝L max -[C1+(1-C1)*Key]*(L max -L min ) (4)
L h ＝L min +[C2+(1-C2)*(1-Key)]*(L max -L min ) (5)
wherein L is max And L min Respectively the maximum value and the minimum value after the image Src _ adj _ gray normalization, wherein the pixel value in the normalized image is less than L t The pixel points of (2) are low-brightness points which are larger than L h The pixel points of (1) are high-brightness points, and the middle is a middle-brightness point. The value range of C1 is set to be 0.5-1, and the value of C2 is set to be smaller than the value of C1. In one embodiment, C1 is 0.9 and C2 is 0.6.
Step S204: it is determined whether the image Src _ adj is a grayscale image.
Step S205: if the image Src _ adj is a gray level image, normalizing the image Src _ adj to obtain a normalized brightness adjustment image Src _ adj _ norm.
Step S206: if the image Src _ adj is a color image, normalizing the three color channels of the image Src _ adj respectively to obtain a normalized brightness adjustment image Src _ adj _ norm.
Step S207: for each pixel point of the normalized image Src _ adj _ norm, the pixel points in a set neighborhood (for example, a window of m × m) with the pixel point as the center are considered, and the ratio rates of low, medium and high brightness points in the window are respectively calculated l ，rate m ，rate h 。
Step S208: mapping pixel values of the low, medium and high brightness points by using the following formula (6); namely:
wherein the parameter s i ，q i ，k i Is a positive value greater than 1, i ∈ [ l, m, h]Corresponding to low, medium and high brightness points, respectively, parameter s i The value increases from low brightness point to high brightness point, and the parameter q i 、k i The value decreases from the low brightness point to the high brightness point, L n Is the pixel value, L, of the image Src _ adj _ norm nmax Is the maximum pixel value, L, of the image Src _ adj _ norm i Is the value after mapping. In one embodiment, s i Has a value in the range of 2 to 15, q i Has a value in the range of 20 to 500, k i The value range of (A) is 20-500. In one embodiment, s of the low luminance point, the medium luminance point and the high luminance point i ，q i ，k i The values of (d) are shown in table 1.
| Is low with | s l ＝2 | q l ＝50 | k l ＝50 |
| In (1) | S m ＝5 | q m ＝45 | k mm ＝45 |
| Height of | S h ＝5 | q h ＝30 | k h ＝30 |
Table 1
Step S209: normalizing each pixel point of the image Src _ adj _ norm according to three groups of different s i ，q i ，k i Value, calculating the mapping value L of low brightness point, middle brightness point and high brightness point l ，L m ，L h And thus obtaining the mapping value of the pixel point of the image Src _ adj as follows:
L＝(L l *rate l +L m *rate m +L h *rate h )*(2^n-1) (7)
in one embodiment, the pixel value mapped image is denoted as Src _ mapping.
As shown in fig. 5, in one embodiment, in the color saturation maintenance in step S300, in the original image Src to be adjusted, the value of the high-brightness pixel point greater than the set brightness threshold is assigned to the corresponding pixel point in the pixel value mapping image Src _ mapping, and the edge pixel point of the assigned high-brightness pixel point in the Src _ mapping image is weighted with the value of the pixel point in the set neighborhood, so as to complete the transition processing.
Step S301: creating a template image Mask, initializing a pixel value of the Mask image to be 0, traversing the Gray image, if the pixel value is greater than (2^ n-1) × D1, for example, when n is 8 and the setting parameter D1 is 0.8, if the pixel value is greater than 200, setting the pixel value of the position corresponding to the pixel value in the Mask image to be 255, and setting the pixel value of the position corresponding to the Src _ map rhoing image to be the value of the pixel point at the same position of the Src image. In this embodiment, the bit width of the template image Mask is equal to or greater than 8.
Step S302: the Mask image is subjected to morphological dilation processing, and is subjected to mean filtering of a set neighborhood (e.g., 11 × 11).
Step S303: traversing the Mask image, and when the pixel value of the Mask image is not 0 and not 255, adjusting the pixel value of the corresponding position in the Src _ mapping image as shown in formula (8), that is:
where, (x, y) represents pixel point positions in the Mask image whose median is not 0 and not 255.
In one embodiment, the image after the color saturation preserving process is denoted as Src _ ret.
As shown in fig. 6 and 7, in one embodiment, the transition processing of the splicing region in step S400 specifically includes the following steps.
Step S401: and setting the first boundary pixel point (x1, y1) and the second boundary pixel point (x2, y2) as boundary pixel points on two sides of the connecting boundary position of the adjacent first exposure subarea and the second exposure subarea respectively.
Step S402: at the (x1, y1) side, in the set splicing transition region, splicing M, such as 20, pixel point sets { (x _ t 1) in the radial direction of the edge i ，y_t1 i ) And i equals to 1,2, …,20}, and processing a pixel point at a corresponding position in the Src _ ret image according to the following formula (9):
wherein Dis1 represents the pixel distance of the current processing pixel distance (x2, y 2); src _ ret (x2, y2) is the pixel of the second boundary pixel (x2, y2) that holds the image at color saturation.
Step S403: at the (x2, y2) side, in the set splicing transition region, splicing M, such as 20, pixel point sets { (x _ t 2) in the radial direction of the edge i ，y_t2 i ) And i equals to 1,2, …,20}, and processing a pixel point at a corresponding position in the Src _ ret image according to the following formula (10):
wherein Dis2 represents the pixel distance of the current processing pixel distance (x1, y 1); src _ ret (x1, y1) is the pixel value of the first boundary pixel (x1, y1) at which the image is maintained at color saturation.
Step S404: and carrying out Gaussian filtering on the connection transition region.
The invention discloses an in-chip partition exposure image fusion method, which is characterized in that brightness adjustment is carried out on an original image to be adjusted; carrying out pixel value mapping on the brightness adjusting image; maintaining the color saturation of the original image to be adjusted; and carrying out transition processing on the adjacent exposure subareas in the connection area, thereby realizing the fusion of the subarea exposure images. The invention can automatically complete the fusion task of the subarea exposure image without manual intervention, is suitable for gray level images and color images, and is suitable for the subarea exposure fusion of continuous video images.
In some embodiments, there is also provided an imaging device comprising a processor and a memory, the memory storing a plurality of instructions, the processor being configured to read the plurality of instructions and execute the on-chip partition exposure image fusion method described above, for example, including: calculating a brightness threshold value to divide pixel points of the brightness adjusting image into low brightness points, medium brightness points and high brightness points; and mapping the pixel value of each pixel point of the brightness adjusting image into the range of [0,2^ n-1] according to the pixel value of the pixel point to be mapped and the ratio and the mapping value of the low, medium and high brightness points in the set neighborhood, so as to obtain a pixel value mapping image. The method can automatically complete the task of mapping the pixel values of the image without manual intervention, and is suitable for gray level images.
In some embodiments, there is also provided a computer readable storage medium storing a plurality of instructions readable by a processor and executable by the processor to perform the on-chip zoned exposure image fusion method described above, for example, comprising: calculating a brightness threshold value to divide pixel points of the brightness adjusting image into low brightness points, medium brightness points and high brightness points; and mapping the pixel value of each pixel point of the brightness adjusting image into the range of [0,2^ n-1] according to the pixel value of the pixel point to be mapped and the ratio and the mapping value of the low, medium and high brightness points in the set neighborhood of the pixel point to be mapped to obtain a pixel value mapping image. The method can automatically complete the task of mapping the pixel values of the image without manual intervention, and is suitable for gray level images.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the recitation of an element by the phrase "comprising an … …" does not exclude the presence of additional like elements in the process, method, article, or apparatus that comprises the element, and further, where similarly-named elements, features, or elements in different embodiments of the invention may have the same meaning, or may have different meanings, that particular meaning should be determined by their interpretation in the embodiment or further by context with the embodiment.
It should be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope herein. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context. Also, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used in this specification, specify the presence of stated features, steps, operations, elements, components, items, species, and/or groups, but do not preclude the presence, or addition of one or more other features, steps, operations, elements, components, species, and/or groups thereof. The terms "or" and/or "as used herein are to be construed as inclusive or meaning any one or any combination. Thus, "A, B or C" or "A, B and/or C" means "any of the following: a; b; c; a and B; a and C; b and C; A. b and C ". An exception to this definition will occur only when a combination of elements, functions, steps or operations are inherently mutually exclusive in some way.
The present invention is not limited to the above preferred embodiments, and any modification, equivalent replacement or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (12)
1. An image pixel value mapping method, comprising the steps of:
calculating a brightness threshold value to divide pixel points of the brightness adjusting image into low brightness points, medium brightness points and high brightness points; and
and mapping the pixel value of each pixel point of the brightness adjusting image into the range of [0,2^ n-1] according to the pixel value of the pixel point to be mapped and the ratio and the mapping value of the low, medium and high brightness points in the set neighborhood of the pixel point to be mapped to obtain a pixel value mapping image.
2. The on-chip zoned-exposure image fusion method according to claim 1, wherein the calculating of the brightness threshold comprises the steps of:
converting the brightness adjustment image into a gray-scale brightness adjustment image, and calculating a logarithmic average lg _ ave of pixel values of the gray-scale brightness adjustment image by the following formula:
wherein Num is the total number of pixel points of the gray brightness adjusting image, Src _ adj _ gray (x, y) represents the pixel value of the gray brightness adjusting image in x rows and y columns, and α is a setting parameter.
3. The on-chip zoned-exposure image fusion method according to claim 2, wherein the calculating the brightness threshold comprises the steps of:
the luminance threshold value Key is calculated according to the following formula, namely:
where, grayMax and grayMin respectively represent the maximum pixel value and the minimum pixel value of the gradation luminance adjustment image.
4. The on-chip divisional exposure image fusion method of claim 1, wherein the division into low luminance points, medium luminance points and high luminance points comprises the steps of:
dividing pixel points of the normalized brightness adjustment image into low brightness points, medium brightness points and high brightness points according to the brightness threshold value by the following formula:
L t ＝L max -[C1+(1-C1)*Key]*(L max -L min )
L h ＝L min +[C2+(1-C2)*(1-Key)]*(L max -L min )
wherein Key is brightness threshold, C1 and C2 are set parameters, L max And L min Respectively the maximum value and the minimum value of the normalized brightness regulating image, wherein the pixel value in the normalized brightness regulating image is less than L t The pixel points of (A) are low-brightness points which are more than L h The pixel points of (1) are high brightness points, and the middle is a middle brightness point.
5. The on-chip subarea exposure image fusion method of claim 4, wherein the value range of C1 is between 0.5 and 1, and the value of C2 is smaller than the value of C1.
6. The on-chip subarea exposure image fusion method of claim 5, wherein a value of C1 is 0.9, and a value of C2 is 0.6.
7. The on-chip zoned-exposure image fusion method according to claim 1, wherein the pixel value mapping comprises the steps of:
if the brightness adjusting image is a gray image, the brightness adjusting image is normalized, and for each pixel point of the normalized brightness adjusting image, the ratio rates of low, medium and high brightness points in the neighborhood are respectively calculated in a first set neighborhood taking the pixel point as the center l ，rate m ，rate h (ii) a And
mapping pixel values of the low, medium and high brightness points by using the following formula;
wherein s is i ，q i ，k i Is a positive value greater than 1, i ∈ [ l, m, h]L, m, h respectively correspond to a low brightness point, a medium brightness point and a high brightness point; s i The value of (a) is increased progressively from a low brightness point to a high brightness point, q i 、k i The value of (A) is decreased from a low brightness point to a high brightness point, L n Adjusting the pixel value, L, of an image for normalized luminance nmax Adjusting the maximum pixel value, L, of an image for normalized luminance i The pixel values of the low-luminance point, the medium-luminance point and the high-luminance point after mapping.
8. The on-chip zoned-exposure image fusion method according to claim 17, wherein s is i Has a value in the range of 2 to 15, q i Has a value in the range of 20 to 500, k i The value range of (a) is 20 to 500.
9. The on-chip zoned-exposure image fusion method according to claim 17, wherein the pixel value mapping comprises the steps of:
if the brightness adjusting image is a color image, normalizing the red, green and blue color channels of the brightness adjusting image respectively to obtain a normalized brightness adjusting image, and then mapping.
10. The on-chip zoned-exposure image fusion method according to claim 17, wherein the pixel value mapping comprises the steps of:
for each pixel point of the normalized brightness adjustment image, three groups of different s are corresponding to the low brightness point, the medium brightness point and the high brightness point i ，q i ，k i Respectively calculating the mapping values L of the low-brightness point, the medium-brightness point and the high-brightness point l ，L m ，L h And then calculating a mapping value L of a pixel point of the brightness adjustment image according to the following formula:
L＝(L l *rate l +L m *rate m +L h *rate h )*255
wherein L is l 、L m 、L h Mapping values, rate, of low brightness point, medium brightness point and high brightness point in the first set neighborhood where the pixel point to be mapped is located l 、rate m 、rate h The ratio of low brightness points, medium brightness points and high brightness points in a first set neighborhood where the pixel points to be mapped are located is determined.
11. An imaging apparatus comprising a processor and a memory, the memory storing at least one instruction, the processor configured to read the at least one instruction and perform the method of any one of claims 1 to 10.
12. A computer storage medium having stored therein at least one instruction that is loaded and executed by a processor to implement the method of any one of claims 1-10.
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Citations (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1857975A2 (en) * | 2006-05-17 | 2007-11-21 | Xerox Corporation | Histogram adjustment for high dynamic range image mapping |
| US20070269132A1 (en) * | 2006-05-17 | 2007-11-22 | Xerox Corporation | Histogram adjustment for high dynamic range image mapping |
| CN101170650A (en) * | 2006-10-24 | 2008-04-30 | 致伸科技股份有限公司 | Highly dynamic image method and system generating specific scenario |
| EP2166509A1 (en) * | 2008-09-22 | 2010-03-24 | Solomon Systech Limited | Method and apparatus of local contrast enhancement |
| US20100157078A1 (en) * | 2008-12-19 | 2010-06-24 | Qualcomm Incorporated | High dynamic range image combining |
| CN101980282A (en) * | 2010-10-21 | 2011-02-23 | 电子科技大学 | Infrared image dynamic detail enhancement method |
| US20130177257A1 (en) * | 2011-06-28 | 2013-07-11 | Dai Nippon Printing Co., Ltd. | Image processing device, image processing method, image processing program and recording medium |
| CN103400342A (en) * | 2013-07-04 | 2013-11-20 | 西安电子科技大学 | Mixed color gradation mapping and compression coefficient-based high dynamic range image reconstruction method |
| US20130322753A1 (en) * | 2012-05-31 | 2013-12-05 | Apple Inc. | Systems and methods for local tone mapping |
| CN103916669A (en) * | 2014-04-11 | 2014-07-09 | 浙江宇视科技有限公司 | High dynamic range image compression method and device |
| US20160127655A1 (en) * | 2014-10-30 | 2016-05-05 | Hisense Mobile Communications Technology Co., Ltd. | Method and device for image taking brightness control and computer readable storage medium |
| EP3051488A1 (en) * | 2015-01-30 | 2016-08-03 | Thomson Licensing | A method and apparatus for inverse-tone mapping a picture |
| CN105850114A (en) * | 2013-12-27 | 2016-08-10 | 汤姆逊许可公司 | Method for inverse tone mapping of an image |
| US20170061590A1 (en) * | 2015-08-25 | 2017-03-02 | Thomson Licensing | Method for inverse tone mapping of a sequence of images |
| CN106504215A (en) * | 2016-11-10 | 2017-03-15 | 中国科学院长春光学精密机械与物理研究所 | A kind of method and device of image enhaucament |
| US9769446B1 (en) * | 2015-03-10 | 2017-09-19 | Lentix, Inc. | Digital image dynamic range processing apparatus and method |
| CN107203974A (en) * | 2016-03-16 | 2017-09-26 | 汤姆逊许可公司 | The methods, devices and systems of HDR HDR to the HDR tones mapping of extension |
| CN107949864A (en) * | 2015-08-25 | 2018-04-20 | 汤姆逊许可公司 | Inverse tone mapping (ITM) based on luminance area |
| CN108009993A (en) * | 2016-10-31 | 2018-05-08 | 乐金显示有限公司 | Handle high dynamic range images method and module and use its display device |
| CN108022223A (en) * | 2017-12-18 | 2018-05-11 | 中山大学 | A kind of tone mapping method based on the processing fusion of logarithmic mapping function piecemeal |
| CN109741288A (en) * | 2019-01-04 | 2019-05-10 | Oppo广东移动通信有限公司 | Image processing method, device, storage medium and electronic equipment |
| CN111563851A (en) * | 2020-03-27 | 2020-08-21 | 中国科学院西安光学精密机械研究所 | Image mapping method based on dynamic Gaussian parameters |
| 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 |
-
2021
- 2021-03-05 CN CN202110253476.6A patent/CN115018714B/en active Active
Patent Citations (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070269132A1 (en) * | 2006-05-17 | 2007-11-22 | Xerox Corporation | Histogram adjustment for high dynamic range image mapping |
| EP1857975A2 (en) * | 2006-05-17 | 2007-11-21 | Xerox Corporation | Histogram adjustment for high dynamic range image mapping |
| CN101170650A (en) * | 2006-10-24 | 2008-04-30 | 致伸科技股份有限公司 | Highly dynamic image method and system generating specific scenario |
| EP2166509A1 (en) * | 2008-09-22 | 2010-03-24 | Solomon Systech Limited | Method and apparatus of local contrast enhancement |
| US20100157078A1 (en) * | 2008-12-19 | 2010-06-24 | Qualcomm Incorporated | High dynamic range image combining |
| CN101980282A (en) * | 2010-10-21 | 2011-02-23 | 电子科技大学 | Infrared image dynamic detail enhancement method |
| US20130177257A1 (en) * | 2011-06-28 | 2013-07-11 | Dai Nippon Printing Co., Ltd. | Image processing device, image processing method, image processing program and recording medium |
| US20130322753A1 (en) * | 2012-05-31 | 2013-12-05 | Apple Inc. | Systems and methods for local tone mapping |
| CN103400342A (en) * | 2013-07-04 | 2013-11-20 | 西安电子科技大学 | Mixed color gradation mapping and compression coefficient-based high dynamic range image reconstruction method |
| CN105850114A (en) * | 2013-12-27 | 2016-08-10 | 汤姆逊许可公司 | Method for inverse tone mapping of an image |
| CN103916669A (en) * | 2014-04-11 | 2014-07-09 | 浙江宇视科技有限公司 | High dynamic range image compression method and device |
| US20160127655A1 (en) * | 2014-10-30 | 2016-05-05 | Hisense Mobile Communications Technology Co., Ltd. | Method and device for image taking brightness control and computer readable storage medium |
| EP3051488A1 (en) * | 2015-01-30 | 2016-08-03 | Thomson Licensing | A method and apparatus for inverse-tone mapping a picture |
| US9769446B1 (en) * | 2015-03-10 | 2017-09-19 | Lentix, Inc. | Digital image dynamic range processing apparatus and method |
| US20170061590A1 (en) * | 2015-08-25 | 2017-03-02 | Thomson Licensing | Method for inverse tone mapping of a sequence of images |
| CN107949864A (en) * | 2015-08-25 | 2018-04-20 | 汤姆逊许可公司 | Inverse tone mapping (ITM) based on luminance area |
| CN107203974A (en) * | 2016-03-16 | 2017-09-26 | 汤姆逊许可公司 | The methods, devices and systems of HDR HDR to the HDR tones mapping of extension |
| CN108009993A (en) * | 2016-10-31 | 2018-05-08 | 乐金显示有限公司 | Handle high dynamic range images method and module and use its display device |
| CN106504215A (en) * | 2016-11-10 | 2017-03-15 | 中国科学院长春光学精密机械与物理研究所 | A kind of method and device of image enhaucament |
| CN108022223A (en) * | 2017-12-18 | 2018-05-11 | 中山大学 | A kind of tone mapping method based on the processing fusion of logarithmic mapping function piecemeal |
| CN109741288A (en) * | 2019-01-04 | 2019-05-10 | Oppo广东移动通信有限公司 | Image processing method, device, storage medium and electronic equipment |
| CN111563851A (en) * | 2020-03-27 | 2020-08-21 | 中国科学院西安光学精密机械研究所 | Image mapping method based on dynamic Gaussian parameters |
| 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 |
Non-Patent Citations (1)
| Title |
|---|
| 余志俊: "高动态范围图像色调映射算法的研究与实现", 《中国优秀硕士学位论文全文数据库信息科技辑》, no. 10, 15 October 2010 (2010-10-15) * |
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