CN112422838A - Multi-exposure-based high dynamic range scene information processing method - Google Patents

Multi-exposure-based high dynamic range scene information processing method Download PDF

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CN112422838A
CN112422838A CN202011016319.5A CN202011016319A CN112422838A CN 112422838 A CN112422838 A CN 112422838A CN 202011016319 A CN202011016319 A CN 202011016319A CN 112422838 A CN112422838 A CN 112422838A
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CN112422838B (en
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仇飞
颜森林
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Nanjing Xiaozhuang University
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    • H04N23/00Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/70Circuitry for compensating brightness variation in the scene
    • H04N23/73Circuitry for compensating brightness variation in the scene by influencing the exposure time
    • HELECTRICITY
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    • H04NPICTORIAL COMMUNICATION, e.g. TELEVISION
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Abstract

The invention provides a multi-exposure-based high dynamic range scene information processing method, which utilizes a reference image to obtain halo pixel points and corresponding pixel values of an input image; filling holes in the halo region graph; converting the reference image into a converted image with the same exposure as the input image; restoring and converting the image according to the input image to obtain a plurality of frames of images with the halation removed; fitting a light fit curve for a plurality of frames of images with the halation removed; generating a high dynamic range image of the scene according to the illumination matching curve; the multi-exposure-based high-dynamic-range scene information processing method provided by the invention not only can effectively remove the halo phenomenon, but also avoids the defect that the detail information of the halo area in the single-frame reference image method is completely determined by the single-frame reference image, and retains and improves the detail information of the generated image in the halo area; and the representation range of the scene image is expanded, and the detail characteristics of a highlight area and a dark area in the scene image are increased.

Description

Multi-exposure-based high dynamic range scene information processing method
Technical Field
The invention relates to the field of digital image processing, in particular to a multi-exposure-based high dynamic range scene information processing method.
Background
A high dynamic range image is an image type that can represent a wide variation in brightness in an actual scene, and the pixel values in the image are proportional to the actual brightness values of corresponding points in the scene, thus better representing the optical characteristics of bright and dark areas in the scene. The range of pixel values to be represented by the dynamic range image is usually large, sometimes hundreds of thousands or even millions, and therefore, the high dynamic range image generally converts the pixel values into single-precision floating-point type decimal or logarithmic numbers for storage.
The high dynamic range of high quality images and the low dynamic range of display devices are the main contradictions, and in order to display high dynamic range images on a ground dynamic range display system, the first problem is to reduce the dynamic range of the images to the dynamic range of the display system. In addition, a series of proper image processing flows are introduced, and the purpose of improving the color space range and the brightness gradation sense of the image is achieved by adjusting certain visual features in the image, so that the detail contrast information lost in the dynamic range compression process of the display equipment can be reduced, and the good appearance of the image is kept.
In addition, the halo phenomenon in high dynamic range image imaging is a problem which is faced, and at present, there are many methods for solving the problem, but the effect is not obvious.
Disclosure of Invention
The present invention aims to solve the above problems by providing a multi-exposure-based high dynamic range scene information processing method, which can not only effectively remove the halo phenomenon, but also avoid the disadvantage that the detail information of the halo region in the single-frame reference image method is completely determined by the single-frame reference image, retain and improve the detail information of the generated image in the halo region, expand the representation range of the scene image, and increase the detail characteristics of the highlight region and the dark region in the scene image.
The adopted technical scheme is as follows:
a multi-exposure-based high dynamic range scene information processing method is characterized by specifically comprising the following steps:
calculating a halo pixel point and a corresponding pixel value of an input image by using an adjacent single-frame reference image, and performing binarization to obtain a halo region image;
obtaining halo pixel points and corresponding pixel values of the input image by using the rest reference images except the adjacent single-frame reference image, and performing binarization to obtain a supplementary light source area image;
processing redundant noise points and filled holes of the light source region map and the supplementary halo region map using morphology;
transforming the reference image into a transformed image with the same exposure as the input image using a luminance mapping function;
restoring and converting the image according to the input image to obtain a plurality of frames of images with the halation removed;
fitting a plurality of frames of images with the halation removed by using a least square principle and a B spline function to obtain an illumination matching curve;
and generating a high dynamic range image of the scene according to the illumination matching curve.
Specifically, the obtaining of the halo pixel point of the input image by using the adjacent single-frame reference image specifically includes:
detecting and solving pixel points and corresponding pixel values of the low exposure image with the brightness value higher than that of the high exposure image;
Figure RE-GDA0002888721910000021
wherein L isKN-1, L is an input image, K ═ 1, 2K(x, y) is the pixel value of the pixel (x, y),
detecting and solving pixel points and corresponding pixel values at the position where the brightness value of the low exposure image is lower than that of the high exposure image;
Figure RE-GDA0002888721910000022
wherein, TK,K+1A threshold value for determining halo;
Figure RE-GDA0002888721910000023
separating out halo pixel points and pixel values M of each frame of imagei(x,y),i=1,2,...N;
Figure RE-GDA0002888721910000024
Specifically, the obtaining of the halo pixel point and the corresponding pixel value of the input image by using the remaining reference images except the adjacent single-frame reference image includes:
Figure RE-GDA0002888721910000031
wherein Ls is the rest of the reference pictures; lr is the input image of the r-th frame, where s is 1, 2, N, r is 1, 2, N (s ≠ r),
Figure RE-GDA0002888721910000032
specifically, the restoring the transformed image according to the input image to obtain the multiple frames of images with the halos removed includes:
the objective function is:
Figure RE-GDA0002888721910000033
wherein Q iss(s ≠ N ≠ 1, 2, …) is from reference graph LsThe input image L is obtained after the conversion of the brightness mapping functionrImages with the same exposure;
Figure RE-GDA0002888721910000034
is the restored image, tau is the luminance mapping function,
Figure RE-GDA0002888721910000035
gradient information representing the image; d (Q)s(x,y),Ts(x, y)) represents Qs(x, y) and Ts(x, y) Euclidean distance; for each pixel point (x, y) in the image, 6 elements need to be extracted, namely pixel values of 3 channels of R, G and B and corresponding gradient information thereof, the parameter beta is to balance the continuity of colors and gradients in the image, and muRepresentation image QsBlock and auxiliary image T in (1)sThe offset between the matching blocks.
Specifically, the auxiliary image is specifically:
Figure RE-GDA0002888721910000036
wherein n (x, y) is a matrix block centered at (x, y) and having a size of P, P is a positive integer of 1-10, Wτ(x, y) represents the luminance mapping function τ (.) value weight, Wμ(j) Weights, normalization coefficients representing the value of the matching block mapping relation mu (.)
Figure RE-GDA0002888721910000041
WtThe value range of (x, y) is 0-1.
In particular, the weights of the values of the mapping relations of the matching blocks are, in particular
Figure RE-GDA0002888721910000042
Wherein,
Figure RE-GDA0002888721910000043
input image LrA matrix block having a size of P x P with (x, y) as a center,
Figure RE-GDA0002888721910000044
is and
Figure RE-GDA0002888721910000045
a matched matrix block.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the multi-exposure-based high-dynamic-range scene information processing method, halo pixel points and corresponding pixel values of an input image are obtained by utilizing a reference image comparison method, and corresponding filling and repairing of halos are carried out, so that the halo phenomenon can be effectively removed, the defect that details of halo regions in a single-frame reference image method are completely determined by a single-frame reference image is avoided, and the details of a generated image in the halo regions are reserved and improved;
2. the method includes the steps that a plurality of frames of images with halation removed are fitted to form an illumination matching curve by means of a least square principle and a B spline function; according to the illumination matching curve, a high dynamic range image of the scene is generated, the representation range of the scene image is expanded, and the detail characteristics of a highlight area and a dark area in the scene image are increased.
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FIG. 1 is a flowchart of a method for processing multi-exposure-based high dynamic range scene information according to an embodiment of the present invention;
FIG. 2 is a diagram of an image captured in a dynamic scene, where (a) is a multi-exposure image captured in the dynamic scene, and (b) is a result diagram of a multi-exposure high dynamic range imaging method in a static scene, according to an embodiment of the present invention;
FIG. 3 is a diagram of halo regions of adjacent images in an embodiment of the present invention;
FIG. 4 is a diagram of halo regions referenced to remaining reference images in an embodiment of the present invention;
FIG. 5 is a diagram of a morphological processed halo region corresponding to that of FIG. 4 in accordance with an embodiment of the present invention;
FIG. 6 is a diagram of a plurality of different exposure images after halo removal according to an embodiment of the present invention;
FIG. 7 is a schematic view of an illumination matching curve according to an embodiment of the present invention;
FIG. 8 is a diagram of generating a high dynamic range image of a scene according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like, indicate orientations and positional relationships based on those shown in the drawings, and are used only for convenience of description and simplicity of description, and do not indicate or imply that the equipment or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention.
Referring to fig. 1, a flowchart of a method for processing high dynamic range scene information based on multiple exposures in this embodiment specifically includes the following steps:
calculating a halo pixel point and a corresponding pixel value of an input image by using an adjacent single-frame reference image, and performing binarization to obtain a halo region image;
obtaining halo pixel points and corresponding pixel values of the input image by using the rest reference images except the adjacent single-frame reference image, and performing binarization to obtain a supplementary light source area image;
processing redundant noise points and filled holes of the light source region map and the supplementary halo region map using morphology;
transforming the reference image into a transformed image with the same exposure as the input image using a luminance mapping function;
restoring and converting the image according to the input image to obtain a plurality of frames of images with the halation removed;
fitting a plurality of frames of images with the halation removed by using a least square principle and a B spline function to obtain an illumination matching curve;
and generating a high dynamic range image of the scene according to the illumination matching curve.
The brightness value of the pixel point of one frame of image is related to the irradiance and the exposure time of a scene, and the relationship between the irradiance E of the image on a camera film or a sensor and the irradiance B of the scene is
Figure RE-GDA0002888721910000061
Wherein h is the focal length of the camera lens;
Figure RE-GDA0002888721910000062
is the angle at which the optical axis deviates from the principal ray; d is the lens aperture size. The relationship between the total exposure amount I and the exposure time t is: the relationship between the brightness value Z of the pixel point of the E × t image, the exposure total amount I and the camera response function f is: z ═ f (i).
From the above formula, when the scene irradiance B is constant and a multi-frame image of the HDR image is synthesized is shot, the camera is kept still, and h are respectively added
Figure RE-GDA0002888721910000064
The exposure parameters are constant, only the lens aperture size d and the exposure time t (a neutral density filter for changing the exposure except external factors) are used for changing the exposure, and the exposure time t has accuracy and repeatability, while the lens aperture size d has no repeatability and the dynamic range of change is limited, so most of the current methods for acquiring images with different exposures are realized by changing the exposure time t. As can be seen from equation (1), the image irradiance E at the (x, y) pixel point of 2 frames of images of the same scene is the same, and we obtain: e1(x,y)=E2(x, y) when Δ t1<Δt2The formula shows that: i is1(x,y)<I2(x, y), and the camera response function f is monotonically increasing, one can obtain: z1(x,y)<Z2(x,y);
Based on this, as the exposure time increases, the pixel values at the same position in different images also increase (except when the pixel values saturate at 255). From the above equation, when the exposure time is increased, the pixel value of the adjacent image at the corresponding position is also increased, and if the pixel value of the low exposure image is larger than the pixel value of the high exposure image, the region where the halo exists is present.
Therefore, the obtaining of the halo pixel point of the input image by using the adjacent single-frame reference image specifically includes:
detecting and solving pixel points and corresponding pixel values of the low exposure image with the brightness value higher than that of the high exposure image;
Figure RE-GDA0002888721910000063
wherein L isKFor the input image, K is 1, 2, N-1, LK(x, y) is the pixel value of the pixel (x, y),
however, only the portion of the low exposure image with the luminance value higher than that of the high exposure image is determined, and the portion of the low exposure image with the luminance value lower than that of the high exposure image cannot be determined;
therefore, detecting and solving pixel points and corresponding pixel values at the position where the brightness value of the low exposure image is lower than that of the high exposure image;
Figure RE-GDA0002888721910000071
wherein, TK,K+1A threshold value for determining halo;
Figure RE-GDA0002888721910000072
separating out halo pixel points and pixel values M of each frame of imagei(x,y),i=1,2,…N;
Figure RE-GDA0002888721910000073
The above formula adopts the halo removing method of the single frame reference image, therefore, what needs to be solved is the halo region between the reference image and other input images except the reference image;
specifically, the method for solving the halo pixel point and the corresponding pixel value of the input image by using the rest reference images except the adjacent single-frame reference image comprises the following steps:
Figure RE-GDA0002888721910000074
wherein Ls is the rest of the reference pictures; lr is the input image of the r-th frame, where s is 1, 2, N, r is 1, 2, N (s ≠ r),
Figure RE-GDA0002888721910000075
in order to improve the detail information of the regions, specifically, the method for synthesizing the HDR image using the single-frame reference image may include:
the objective function is:
Figure RE-GDA0002888721910000081
wherein Q iss(s ≠ N ≠ 1, 2, …) is from reference graph LsThe input image L is obtained after the conversion of the brightness mapping functionrImages with the same exposure;
Figure RE-GDA0002888721910000082
is the restored image, tau is the luminance mapping function,
Figure RE-GDA0002888721910000083
gradient information representing the image; d (Q)s(x,y),Ts(x, y)) represents Qs(x, y) and Ts(x, y) Euclidean distance; for each pixel point (x, y) in the image, 6 elements need to be extracted, namely pixel values of 3 channels of R, G and B and corresponding gradient information thereof, the parameter beta is to balance color in the image and continuity of gradient, and the mu represents the image QsBlock and auxiliary image T in (1)sThe offset between the matching blocks.
Specifically, the auxiliary image is specifically:
Figure RE-GDA0002888721910000084
wherein n (x, y) is a matrix block centered at (x, y) and having a size of P, P is a positive integer of 1-10, Wτ(x, y) represents the luminance mapping function τ (.) value weight, Wμ(j) Weights, normalization coefficients representing the value of the matching block mapping relation mu (.)
Figure RE-GDA0002888721910000085
WτThe value range of (x, y) is 0-1.
In particular, the weights of the values of the mapping relations of the matching blocks are, in particular
Figure RE-GDA0002888721910000091
Wherein,
Figure RE-GDA0002888721910000092
input image LrA matrix block having a size of P x P with (x, y) as a center,
Figure RE-GDA0002888721910000093
is and
Figure RE-GDA0002888721910000094
a matched matrix block.
After halo is removed, a plurality of frames of different exposure images equivalent to a static scene are obtained, and after a high dynamic range image can be synthesized by using a high dynamic range image imaging method aiming at the static scene, because a common display cannot display, a tone mapping method is needed to compress a dynamic range to obtain a final result image;
in the embodiment, for a plurality of frames of different exposure images, an illumination matching curve is fitted by using a least square principle and a B spline function; and generating a high dynamic range image of the scene according to the illumination matching curve.
The illumination matching curve of the imaging system is a relation curve between the exposure of a camera and the pixel value of an image and is a key for synthesizing a high dynamic range image; as shown in fig. 7, there are 5 images, each image has 4 sampling points, the same shape symbol in the figure represents the same group of points in different images, and each group of sampling points corresponds to a characteristic curve. The abscissa is the log value lnH of the exposure amount, and the ordinate is the pixel value V. Since the absolute value of the exposure is unknown, assuming that the illumination value E for each set of points is 1, the position in the abscissa direction (lnH) depends on the exposure time t. In practice, the illumination values E of different points are different, and the N (4) curves can be smoothly spliced together by translating left and right, so as to obtain the illumination fit curve of the imaging system, but the horizontal position of the synthesized curve cannot be determined yet and needs to be defined artificially. The synthesized illumination matching curve shows nonlinear characteristics in a bright area and a dark area, and a high dynamic range image of a scene is generated according to the illumination matching curve.
The experimental results are as follows:
fig. 3 is a diagram of the halo region calculated for the adjacent input image in fig. 2 a. And detecting a part of adjacent image halo regions by utilizing the relationship that the pixel values of the pixel points at the same position of the adjacent 2 frames of images are increased (except the situation that the pixel values are saturated) along with the increase of the exposure time. Because only the area which is in the moving object and a small amount of pixel points caused by errors in the image acquisition process violate the relation, the noise points contained in the halo area are very few, and the threshold value adopted in the judgment of the halo areas of the rest adjacent images is obtained by multiple iterative optimization, so that the detected halo area contains as few noise points as possible. The halo regions are judged for different adjacent 2 frames of images by the multi-iteration mode, different thresholds are used, the halo regions are more accurate than the halo regions judged by the fixed threshold setting mode, and a large number of noise points are reduced. Compared with other methods, the halo region determined by the embodiment contains relatively fewer noise points irrelevant to the moving object, and the accuracy of the obtained halo region is further improved. As shown in fig. 4, in order to obtain the halo region map of the selected input image of the 1 st frame in the 2 nd row in fig. 2a as the reference image, the image is compared with other input images; FIG. 5 is a diagram showing the result of removing unwanted noise points and filling holes; the result of the matching block adopted by the embodiment is more accurate, the specific halo region distinguished by the embodiment is combined to improve the effect of enhancing the detail information, a plurality of frames of different exposure images equivalent to a static scene are obtained after halo is removed, and as shown in fig. 6, the exposure of the removed moving object image is basically consistent with that of the original input image; fig. 7 is a high dynamic range image of a generated scene, which retains and improves detail information of the generated image in a halo region, enlarges the representation range of the scene image, and increases the detail characteristics of a highlight region and a dark region in the scene image.
According to the multi-exposure-based high-dynamic-range scene information processing method, halo pixel points and corresponding pixel values of an input image are obtained by utilizing a reference image comparison method, and corresponding filling and repairing of halos are carried out, so that the halo phenomenon can be effectively removed, the defect that details of halo regions in a single-frame reference image method are completely determined by a single-frame reference image is avoided, and the details of a generated image in the halo regions are reserved and improved; in addition, the invention provides a method for fitting a plurality of frames of images without halation by using a least square principle and a B spline function to obtain an illumination matching curve; according to the illumination matching curve, a high dynamic range image of the scene is generated, the representation range of the scene image is expanded, and the detail characteristics of a highlight area and a dark area in the scene image are increased.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (6)

1. A multi-exposure-based high dynamic range scene information processing method is characterized by specifically comprising the following steps:
calculating a halo pixel point and a corresponding pixel value of an input image by using an adjacent single-frame reference image, and performing binarization to obtain a halo region image;
obtaining halo pixel points and corresponding pixel values of the input image by using the rest reference images except the adjacent single-frame reference image, and performing binarization to obtain a supplementary light source area image;
processing redundant noise points and filled holes of the light source region map and the supplementary halo region map using morphology;
transforming the reference image into a transformed image with the same exposure as the input image using a luminance mapping function;
restoring and converting the image according to the input image to obtain a plurality of frames of images with the halation removed;
fitting a plurality of frames of images with the halation removed by using a least square principle and a B spline function to obtain an illumination matching curve;
and generating a high dynamic range image of the scene according to the illumination matching curve.
2. The method as claimed in claim 1, wherein the obtaining of the halo pixel of the input image by using the adjacent single-frame reference image specifically comprises:
detecting and solving pixel points and corresponding pixel values of the low exposure image with the brightness value higher than that of the high exposure image;
Figure RE-FDA0002827848080000011
wherein L isKFor the input image, K is 1, 2, N-1, LK(x, y) is the pixel value of the pixel (x, y),
detecting and solving pixel points and corresponding pixel values at the position where the brightness value of the low exposure image is lower than that of the high exposure image;
Figure RE-FDA0002827848080000012
wherein, TK,K+1A threshold value for determining halo;
Figure RE-FDA0002827848080000013
separating out halo pixel points and pixel values M of each frame of imagei(x,y),i=1,2,...N;
Figure RE-FDA0002827848080000021
3. The method as claimed in claim 2, wherein the obtaining of the halo pixel and the corresponding pixel value of the input image by using the remaining reference images except the adjacent single-frame reference image comprises:
Fs,r(x,y)=Ms(x,y)+Mr(x,y)+Gs,r(x,y)
wherein Ls is the rest of the reference pictures; lr is the input image of the r-th frame, where s is 1, 2, N, r is 1, 2, N (s ≠ r),
Figure RE-FDA0002827848080000022
4. the method as claimed in claim 2, wherein the restoring the transformed image according to the input image to obtain the multiple frames of images with the halo removed comprises:
the objective function is:
Figure RE-FDA0002827848080000023
wherein Q iss(s ≠ N ≠ 1, 2, …) is from reference graph LsThe input image L is obtained after the conversion of the brightness mapping functionrImages with the same exposure;
Figure RE-FDA0002827848080000024
is the restored image, tau is the luminance mapping function,
Figure RE-FDA0002827848080000025
gradient information representing the image; d (Q)s(x,y),Ts(x, y)) represents Qs(x, y) and Ts(x, y) Euclidean distance; for each pixel point (x, y) in the image, 6 elements need to be extracted, namely pixel values of 3 channels of R, G and B and corresponding gradient information thereof, the parameter beta is to balance color in the image and continuity of gradient, and the mu represents the image QsBlock and auxiliary image T in (1)sThe offset between the matching blocks.
5. The method as claimed in claim 4, wherein the auxiliary image is specifically:
Figure RE-FDA0002827848080000031
wherein n (x, y) is a matrix block centered at (x, y) and having a size of P, P is a positive integer of 1-10, Wτ(x, y) represents the luminance mapping function τ (.) value weight, Wμ(j) Weights, normalization coefficients representing the value of the matching block mapping relation mu (.)
Figure RE-FDA0002827848080000032
WτThe value range of (x, y) is 0-1.
6. The method of claim 5, wherein the weights of the block mapping relationship values are matched, specifically the weights are matched
Figure RE-FDA0002827848080000033
2. Wherein,
Figure RE-FDA0002827848080000034
input image LrA matrix block having a size of P x P with (x, y) as a center,
Figure RE-FDA0002827848080000035
is and
Figure RE-FDA0002827848080000036
a matched matrix block.
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