CN113139947A - Image processing method and device, electronic equipment and storage medium - Google Patents
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Abstract
The present disclosure relates to an image processing method and apparatus, an electronic device, and a storage medium, the method including: determining linear estimation parameters of a plurality of image positions of an image to be processed according to the image to be processed and a guide image of the image to be processed; obtaining a structural diagram of the image to be processed according to the guide image and the linear estimation parameters of the plurality of image positions; and fusing the structure diagram with a texture diagram of the image to be processed to obtain an image processing result of the image to be processed, wherein a linear estimation parameter of any image position in the image to be processed is associated with a region variance value and a region entropy value of a first image region in the guide image, and the first image region comprises an image region which is in the guide image and takes the image position as a center and has a preset size. The embodiment of the disclosure can improve the image processing effect.
Description
Technical Field
The present disclosure relates to the field of computer vision technologies, and in particular, to an image processing method and apparatus, an electronic device, and a storage medium.
Background
In various image processing tasks and computer vision tasks (such as image defogging, dim light enhancement, image stitching, and the like), it is often necessary to filter an image to achieve corresponding processing. Among them, edge-preserving filtering is one of the most commonly used image filtering techniques at present. In the related art, edge protection filtering has two modes, namely local filtering and global optimization, the processing effect of global optimization is better, but the computational complexity is too high, and the efficiency is lower: the local filtering is simple and convenient to calculate, but the problems of artifacts and the like are easily generated, so that the processing effect is poor.
Disclosure of Invention
The present disclosure proposes an image processing technical solution.
According to an aspect of the present disclosure, there is provided an image processing method including: determining linear estimation parameters of a plurality of image positions of an image to be processed according to the image to be processed and a guide image of the image to be processed; obtaining a structural diagram of the image to be processed according to the guide image and the linear estimation parameters of the plurality of image positions; fusing the structure chart with the texture chart of the image to be processed to obtain an image processing result of the image to be processed,
the linear estimation parameter of any image position in the image to be processed is associated with a region variance value and a region entropy value of a first image region in the guide image, and the first image region comprises an image region with a preset size and taking the image position as a center in the guide image.
In a possible implementation manner, the determining, according to the image to be processed and the guide image, a linear estimation parameter of a plurality of image positions of the image to be processed includes: aiming at any image position of the image to be processed, determining a first image area and a second image area corresponding to the image position, wherein the second image area comprises an image area which is centered at the image position and has a preset size in the image to be processed; respectively determining the region parameters of the first image region and the second image region, wherein the region parameters comprise the region variance value, the region entropy value and the first region mean value of the pixel points in the first image region, the second region mean value of the pixel points in the second image region, and the third region mean value of the fusion region of the first image region and the second image region; and determining a linear estimation parameter of the image position according to the region parameter.
In a possible implementation manner, the determining the region parameters of the first image region and the second image region respectively includes: fusing the first image area and the second image area to obtain a fused area; and determining the average value of the third area of the pixel points in the fusion area.
In a possible implementation manner, the determining the region parameters of the first image region and the second image region respectively includes: determining the occurrence probability of each pixel point in the first image area in the guide image according to the brightness histogram of the guide image; and determining the region entropy value according to the occurrence probability of each pixel point in the first image region.
In a possible implementation manner, the determining the linear estimation parameter of the image position according to the region parameter includes: determining a first parameter of the image position according to the region variance value, the region entropy value, the first region mean value, the second region mean value and the third region mean value; and determining a second parameter of the image position according to the first area mean value, the second area mean value and the first parameter.
In one possible implementation, the method further includes: and performing linear filtering on the image to be processed to obtain a guide image of the image to be processed.
In one possible implementation, the method further includes: and decomposing the image to be processed to obtain a texture map of the image to be processed.
According to an aspect of the present disclosure, there is provided an image processing apparatus including:
the parameter determining module is used for determining linear estimation parameters of a plurality of image positions of the image to be processed according to the image to be processed and the guide image of the image to be processed;
the structure chart acquisition module is used for acquiring a structure chart of the image to be processed according to the guide image and the linear estimation parameters of the plurality of image positions;
a result determining module, configured to fuse the structure diagram with the texture map of the image to be processed to obtain an image processing result of the image to be processed,
the linear estimation parameter of any image position in the image to be processed is associated with a region variance value and a region entropy value of a first image region in the guide image, and the first image region comprises an image region with a preset size and taking the image position as a center in the guide image.
In one possible implementation manner, the parameter determining module includes:
the area determining submodule is used for determining a first image area and a second image area corresponding to the image positions aiming at any image position of the image to be processed, wherein the second image area comprises an image area which is centered at the image position and has a preset size in the image to be processed;
a region parameter determining submodule, configured to determine region parameters of the first image region and the second image region, respectively, where the region parameters include a region variance value, a region entropy value, and a first region mean value of a pixel point in the first image region, a second region mean value of a pixel point in the second image region, and a third region mean value of a fusion region of the first image region and the second image region;
and the linear parameter determining submodule is used for determining a linear estimation parameter of the image position according to the region parameter.
In one possible implementation, the region parameter determining submodule is configured to: fusing the first image area and the second image area to obtain a fused area; and determining the average value of the third area of the pixel points in the fusion area.
In one possible implementation, the region parameter determining submodule is configured to: determining the occurrence probability of each pixel point in the first image area in the guide image according to the brightness histogram of the guide image; and determining the region entropy value according to the occurrence probability of each pixel point in the first image region.
In a possible implementation manner, the linear estimation parameter includes a first parameter and a second parameter, and the linear parameter determination sub-module is configured to: determining a first parameter of the image position according to the region variance value, the region entropy value, the first region mean value, the second region mean value and the third region mean value; and determining a second parameter of the image position according to the first area mean value, the second area mean value and the first parameter.
In one possible implementation, the apparatus further includes: and the linear filtering module is used for performing linear filtering on the image to be processed to obtain a guide image of the image to be processed.
In one possible implementation, the apparatus further includes: and the image decomposition module is used for decomposing the image to be processed to obtain a texture map of the image to be processed.
According to an aspect of the present disclosure, there is provided an electronic device including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.
According to an aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described method.
In the embodiment of the disclosure, the linear estimation parameters can be determined according to the image to be processed and the guide image; obtaining a structural diagram of the image to be processed according to the guide image and the linear estimation parameters; the structure diagram and the texture diagram are fused to obtain an image processing result, and the linear estimation parameters are associated with the region variance value and the region entropy value, so that the definition of the image edge can be better kept, and the image processing effect is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.
Fig. 1 shows a flowchart of an image processing method according to an embodiment of the present disclosure.
Fig. 2 illustrates a block diagram of an image processing apparatus according to an embodiment of the present disclosure.
Fig. 3 shows a block diagram of an electronic device in accordance with an embodiment of the disclosure.
Fig. 4 shows a block diagram of an electronic device in accordance with an embodiment of the disclosure.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.
The guiding filtering is a local filtering algorithm of an edge protection filtering technology, and a basic idea of the guiding filtering is that each pixel point in an image can be obtained by fitting an M × M (M is an integer greater than 1, for example, a value is 3, 5, 7, and the like) local window and a linear regression model, and a regularization constraint term is added on the basis of the linear regression model to reduce the overfitting problem. However, the weight of the regularization constraint term is fixed, which tends to create artifact problems in the edge regions.
According to the image processing method disclosed by the embodiment of the disclosure, the linear estimation parameter of the image position can be determined based on the region variance value and the region entropy value of the guide image region corresponding to the image position, and the weight of the regularization constraint term is automatically adjusted through an entropy-variance combined adaptive processing mode, so that artifacts possibly occurring in the edge region of the image are reduced, and the processing effect of the image is improved.
Fig. 1 illustrates a flowchart of an image processing method according to an embodiment of the present disclosure, which includes, as illustrated in fig. 1:
in step S11, determining linear estimation parameters of a plurality of image positions of an image to be processed according to the image to be processed and a guide image of the image to be processed;
in step S12, obtaining a structure diagram of the image to be processed according to the guide image and the linear estimation parameters of the plurality of image positions;
in step S13, the structure diagram is fused with the texture map of the image to be processed to obtain the image processing result of the image to be processed,
the linear estimation parameter of any image position in the image to be processed is associated with a region variance value and a region entropy value of a first image region in the guide image, and the first image region comprises an image region with a preset size and taking the image position as a center in the guide image.
In one possible implementation, the image processing method may be performed by an electronic device such as a terminal device or a server, the terminal device may be a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, or the like, and the method may be implemented by a processor calling a computer readable instruction stored in a memory. Alternatively, the method may be performed by a server.
For example, the image to be processed may be any image, such as a scene image captured by an image capturing device, an image downloaded from a network, and the like. The image processing task for the image to be processed may be of any type, such as image defogging, dim light enhancement, contrast enhancement, tone mapping, high dynamic range HDR imaging, and the like, and the present disclosure is not limited to the type and source of the image to be processed, nor to the specific type of the image processing task.
In one possible implementation, an edge-preserving filtering algorithm may be used to decompose the image to be processed into two layers, referred to as a base layer and a detail layer, where the base layer includes overall structural information of the image (e.g., location, layout, etc. of objects in the image), and the detail layer includes texture detail information of the image (e.g., texture, orientation, etc. of objects in the image).
In this case, the image to be processed x (p) can be expressed as:
X(p)＝Z(p)+D(p) (1)
in formula (1), z (p) may represent a base layer; d (p) may represent a detail layer; p may represent any image location in the image. In the case of image processing with guided filtering, the base layer can be estimated by linear transformation of the guided image:
in the formula (2), the first and second groups,
may represent a linear transform estimate of the base layer, g (p) may represent a guide image; a isp′And bp′The linear estimated parameters of the linear regression model at the image position p can be represented.
In one possible implementation, the guide image is used to filter out a target component in the image to be processed (e.g., filter out noise in the image to be processed). Linear filtering (for example, block filtering, mean filtering, gaussian filtering, or the like) may be performed on the image to be processed to obtain a guide image; an image corresponding to the image processing task (e.g., an image that has been subjected to dim enhancement) may also be selected as the guide image based on the category of the image processing task; the image to be processed itself may also be used as a guide image. The present disclosure does not limit the source and processing manner of the guide image.
In one possible implementation manner, in step S11, linear estimation parameters at each image position of the image to be processed may be determined respectively according to the image to be processed and the guide image. Wherein the linear estimation parameter a of the image position p can be calculated by the following linear regression modelp′And bp′：
In the formula (3), the first and second groups,
can represent the side length is zeta with the image position p as the center1A rectangular window (e.g., 3, 5, 7, 11, 17, etc.) including K ═ ζ1×ζ1An image position q represents any pixel point in the rectangular window;
regularization to prevent overfitting problems can be representedA constraint term;
the weight of the regularization constraint term can be represented, and lambda represents a regularization coefficient (for example, the value is 0.1, 0.01 and the like); gamma-shapedG(p') may be referred to as an "entropy-variance joint adaptation factor" for automatically adjusting the size of the regularization coefficients.
In one possible implementation, ΓG(p') can be represented as:
in the formula (4), the first and second groups,
rectangular window in representable guide image
Cross entropy-variance factor (cross entropy-variance factor); n may represent the number of pixels in the guide image; i may represent any location in the guide image;
the cross entropy-variance factor of the rectangular window corresponding to position i in the guide image can be represented. As can be seen from equation (4), gammaG(p') is
The result of the normalization process.
In one possible implementation, the cross entropy-variance factor
Can be expressed as:
in the formula (5), the first and second groups,
rectangular window in representable guide image
The regional variance value (or called local variance value) of the inner pixel points;
rectangular window in representable guide image
The region entropy (or local entropy) of the pixel points within.
By substituting equations (4) and (5) into equation (3), the linear estimation parameter a of the image position p can be obtainedp′And bp′The obtained linear estimation parameter ap′And bp′And guiding rectangular windows in images
The region variance value (which may be referred to as the first image region) is associated with the region entropy value. The first image area includes an image area of a preset size (e.g., 3 × 3, 5 × 5, etc.) centered on the image position p in the guide image.
For the region near the image edge, the region variance value is usually much larger than the region entropy value; for smooth regions inside the image (e.g. texture detail regions or noisy background regions), the region entropy value is usually larger than the region variance value. Thus, the factor Γ of the edge regionG(p') will be greater than the factor Γ of the smooth regionG(p'). In this case, the halo artifact near the edge and the over-smoothing problem on the details are reduced, so that the edge definition can be better maintained, and the image processing effect is improved.
In a possible implementation manner, in step S12, a structure diagram of the image to be processed can be obtained according to the linear estimation parameters of the guide image and the positions of the plurality of images. That is, the linear estimation parameter a at the obtained image position pp′And bp′Then, a linear estimation of the image position p can be calculated according to equation (2). And processing each image position respectively to obtain a linear estimation result of the whole image to be processed, namely a basic layer of the image to be processed. The base layer includes the overall structure information of the image, and may be referred to as a structure diagram of the image to be processed.
In the foregoing description, an edge-preserving filtering algorithm may be employed to decompose the image to be processed into a base layer and a detail layer. The detail layer includes texture detail information of the image, which may be referred to as a texture map of the image to be processed.
In one possible implementation manner, in step S13, the structure diagram of the image to be processed may be fused with the texture map to obtain the image processing result of the image to be processed.
In one possible implementation, the texture map and the texture map may be directly summed to obtain a processed image as an image processing result.
In one possible implementation manner, different weights may be set for the texture map and the structure map according to the type of the image processing task (for example, in the image enhancement task, the weights of the texture map and the structure map are set to 1 and 2, respectively, to achieve the enhancement of the image texture details). And further, carrying out weighted summation on the texture map and the structure map to obtain a processed image as an image processing result. The present disclosure does not limit the way in which the texture map and the texture map are fused.
The entropy-variance combined adaptive factor introduced by the embodiment of the disclosure can better distinguish the edge region of the image from the smooth region in the image, and also has better anti-noise capability. Therefore, the image processing result obtained in step S13 can reduce halo artifacts near the edges of the image and improve the sharpness of the edges of the image while retaining the detail information of the original image.
For example, when the method is applied to an image enhancement task such as contrast enhancement, the image processing result obtained in step S13 has better visual quality and quantitative performance.
According to the embodiment of the disclosure, the linear estimation parameters can be determined according to the image to be processed and the guide image; obtaining a structural diagram of the image to be processed according to the guide image and the linear estimation parameters; the structure diagram and the texture diagram are fused to obtain an image processing result, and the linear estimation parameters are associated with the region variance value and the region entropy value, so that the definition of the image edge can be better kept, and the image processing effect is improved.
The following is a description of an image processing method according to an embodiment of the present disclosure.
In one possible implementation manner, before step S11, the image processing method according to the embodiment of the present disclosure may further include: and performing linear filtering on the image to be processed to obtain a guide image of the image to be processed.
That is, the guide image of the image to be processed may be obtained by a linear filtering method, and information such as noise in the image to be processed is filtered out from the guide image, so that the information in the image is smoother, so as to guide subsequent processing of the image to be processed.
The linear filtering method may be, for example, block filtering, mean filtering, gaussian filtering, etc., which is not limited by the present disclosure.
In step S11, linear estimation parameters for a plurality of image positions of the image to be processed may be determined according to the image to be processed and the guide image. In one possible implementation, step S11 may include:
aiming at any image position of the image to be processed, determining a first image area and a second image area corresponding to the image position, wherein the second image area comprises an image area which is centered at the image position and has a preset size in the image to be processed;
respectively determining the region parameters of the first image region and the second image region, wherein the region parameters comprise the region variance value, the region entropy value and the first region mean value of the pixel points in the first image region, the second region mean value of the pixel points in the second image region, and the third region mean value of the fusion region of the first image region and the second image region;
and determining a linear estimation parameter of the image position according to the region parameter.
For example, for any image position in the image to be processed, a window region corresponding to the image position may be determined first, including the first image region and the second image region. The first image area comprises an image area which is in the guide image, takes the image position as the center and is in a preset size; the second image area comprises an image area with a preset size and taking the image position as the center in the image to be processed. The preset size may be set to 3 × 3, 5 × 3, 7 × 7, 11 × 11, 17 × 17, etc., for example, and the present disclosure does not limit specific values of the preset size.
In one possible implementation, in a case where the image position is near an image edge, if the first image area and the second image area are beyond the image range of the guide image and the image to be processed, a portion beyond the image range may be padded with zeros.
In a possible implementation manner, the region parameters of the first image region and the second image region can be determined separately, so as to calculate the linear estimation parameter a of the image position pp′And bp′. The region parameters include a region variance value, a region entropy value and a first region mean value of pixel points in the first image region, a second region mean value of pixel points in the second image region, and a third region mean value of a fusion region of the first image region and the second image region.
In one possible implementation, the regional variance value of the pixel points in the first image region is the value in formula (5)
Can be expressed as:
in the formula (6), the first and second groups,
can represent a rectangular window in the guide image corresponding to the image position p
The regional variance of the inner pixel points; n may represent the number of pixels in the guide image; j may represent any image position in the guide image;
the regional variance of the pixel points in the guide image within the rectangular window corresponding to image position j can be represented. As can be seen from the equation (6),
is that
The result of the normalization process.
In a possible implementation manner, the step of determining the region parameters of the first image region and the second image region respectively may include:
determining the occurrence probability of each pixel point in the first image area in the guide image according to the brightness histogram of the guide image;
and determining the region entropy value according to the occurrence probability of each pixel point in the first image region.
Wherein the region entropy of the pixel points in the first image region is the region entropy of formula (5)
Can be expressed as:
in formula (7), K may represent the number of pixel points in the first image region;
the probability of occurrence of the kth pixel point in the first image region can be represented.
In one possible implementation, the guide image may be processed to obtain a luminance histogram of the guide image, and the present disclosure does not limit the specific processing manner.
In a possible implementation manner, according to the luminance histogram, the luminance value of each pixel point in the first image region and the occurrence probability of each pixel point in the guide image can be determined; further, the region entropy of the first image region can be calculated by equation (7).
In this way, the region entropy value of the image region can be obtained for subsequent calculation of the linear estimation parameter.
In a possible implementation manner, the pixel values in the first image region and the pixel values in the second image region may be averaged respectively to obtain a first region mean value and a second region mean value in the region parameter.
In a possible implementation manner, the step of determining the region parameters of the first image region and the second image region respectively may include:
fusing the first image area and the second image area to obtain a fused area;
and determining the average value of the third area of the pixel points in the fusion area.
That is, the pixel values of the corresponding positions of the first image area and the second image area can be subjected to dot multiplication to obtain a fusion area; and averaging the pixel values in the fusion region to obtain a third region average value in the region parameters.
In this way, the region mean value of the region can be fused, and the association between the guide image and the image to be processed is realized.
In one possible implementation, the linear estimation parameter includes a first parameter ap′And a second parameter bp′. Wherein, according to the region parameter, the step of determining the linear estimation parameter of the image position may include:
determining a first parameter of the image position according to the region variance value, the region entropy value, the first region mean value, the second region mean value and the third region mean value;
and determining a second parameter of the image position according to the first area mean value, the second area mean value and the first parameter.
As shown above, by substituting equations (4) - (5) into equation (3), the linear estimation parameter a of the image position can be obtainedp′And bp′Expressed as:
in the equations (8) and (9),
may represent a first region mean;
may represent a second region mean;
may represent a third area mean; the operator ° may represent a dot product.
In one possible implementation, the factor Γ may be derived based on equation (4) from the region variance value and the region entropy valueG(p'); according to a factor ΓG(p'), first region mean value
Second area mean value
And third area mean value
I.e. the first parameter a for determining the image position based on equation (8)p′(ii) a Further, according to the first area mean value
Second area mean value
And a first parameter ap′I.e. the second parameter b of the image position can be determined based on equation (9)p′。
In this way, two linear estimation parameters of any image position can be respectively determined, and the linear estimation parameters are associated with the region variance value and the region entropy value, so that the image processing effect can be improved.
In a possible implementation manner, the processing manner may be adopted to process each image position in the image to obtain a linear estimation parameter of the whole image to be processed; in step S12, a structure diagram of the image to be processed is calculated based on the formula (2) according to the guide image and the linear estimation parameter.
In one possible implementation manner, the image processing method according to the embodiment of the present disclosure may further include:
and decomposing the image to be processed to obtain a texture map of the image to be processed.
As mentioned above, an edge protection filtering algorithm may be used to decompose the image to be processed into a base layer and a detail layer, and the detail layer is used as a texture map of the image to be processed.
In a possible implementation manner, filtering may also be directly performed on the image to be processed, so as to remove information such as noise in the image to be processed, and retain texture detail information in the image to be processed, so as to obtain a texture map of the image to be processed. The filtering manner may be a linear restorable filtering, and the disclosure does not limit the specific filtering manner.
In one possible implementation manner, after obtaining the texture map of the image to be processed, in step S13, the structure map of the image to be processed may be fused with the texture map to obtain the image processing result of the image to be processed.
In one possible implementation, the texture map and the texture map may be directly summed to obtain a processed image as an image processing result. Different weights may be set for the texture map and the texture map depending on the type of the image processing task (for example, in the image enhancement task, the weights of the texture map and the texture map are set to 1 and 2, respectively). And carrying out weighted summation on the texture map and the structure map to obtain a processed image as an image processing result. The present disclosure does not limit the way in which the texture map and the texture map are fused.
According to the image processing method disclosed by the embodiment of the disclosure, an extended guide filtering mode is provided, and a linear estimation parameter of an image position can be determined based on a region variance value and a region entropy value of a guide image region corresponding to the image position; the weight of the regularization constraint item of the linear estimation parameter is automatically adjusted through an entropy-variance combined self-adaptive processing mode, and the regularization weight can be adjusted to different degrees according to different image structures, so that the problem of artifacts caused by the fixation of the regularization weight in the guide filtering is solved.
The image processing method disclosed by the embodiment of the disclosure can be applied to the fields of artificial intelligence, image processing, machine vision and the like, can realize image processing tasks such as image defogging, dim light enhancement, contrast enhancement, tone mapping, high dynamic range HDR imaging, image splicing and the like, can reduce halo artifacts near the edges of images and the problem of excessive smoothness on details, can better keep the definition of the edges, and can improve the image processing effect.
It is understood that the above-mentioned method embodiments of the present disclosure can be combined with each other to form a combined embodiment without departing from the logic of the principle, which is limited by the space, and the detailed description of the present disclosure is omitted. Those skilled in the art will appreciate that in the above methods of the specific embodiments, the specific order of execution of the steps should be determined by their function and possibly their inherent logic.
In addition, the present disclosure also provides an image processing apparatus, an electronic device, a computer-readable storage medium, and a program, which can be used to implement any one of the image processing methods provided by the present disclosure, and the descriptions and corresponding descriptions of the corresponding technical solutions and the corresponding descriptions in the methods section are omitted for brevity.
Fig. 2 shows a block diagram of an image processing apparatus according to an embodiment of the present disclosure, which, as shown in fig. 2, includes:
a parameter determining module 21, configured to determine linear estimation parameters of a plurality of image positions of an image to be processed according to the image to be processed and a guide image of the image to be processed;
a structure chart obtaining module 22, configured to obtain a structure chart of the image to be processed according to the guide image and the linear estimation parameters of the multiple image positions;
a result determining module 23, configured to fuse the structure diagram with the texture map of the image to be processed to obtain an image processing result of the image to be processed,
the linear estimation parameter of any image position in the image to be processed is associated with a region variance value and a region entropy value of a first image region in the guide image, and the first image region comprises an image region with a preset size and taking the image position as a center in the guide image.
In one possible implementation manner, the parameter determining module includes:
the area determining submodule is used for determining a first image area and a second image area corresponding to the image positions aiming at any image position of the image to be processed, wherein the second image area comprises an image area which is centered at the image position and has a preset size in the image to be processed;
a region parameter determining submodule, configured to determine region parameters of the first image region and the second image region, respectively, where the region parameters include a region variance value, a region entropy value, and a first region mean value of a pixel point in the first image region, a second region mean value of a pixel point in the second image region, and a third region mean value of a fusion region of the first image region and the second image region;
and the linear parameter determining submodule is used for determining a linear estimation parameter of the image position according to the region parameter.
In one possible implementation, the region parameter determining submodule is configured to: fusing the first image area and the second image area to obtain a fused area; and determining the average value of the third area of the pixel points in the fusion area.
In one possible implementation, the region parameter determining submodule is configured to: determining the occurrence probability of each pixel point in the first image area in the guide image according to the brightness histogram of the guide image; and determining the region entropy value according to the occurrence probability of each pixel point in the first image region.
In a possible implementation manner, the linear estimation parameter includes a first parameter and a second parameter, and the linear parameter determination sub-module is configured to: determining a first parameter of the image position according to the region variance value, the region entropy value, the first region mean value, the second region mean value and the third region mean value; and determining a second parameter of the image position according to the first area mean value, the second area mean value and the first parameter.
In one possible implementation, the apparatus further includes: and the linear filtering module is used for performing linear filtering on the image to be processed to obtain a guide image of the image to be processed.
In one possible implementation, the apparatus further includes: and the image decomposition module is used for decomposing the image to be processed to obtain a texture map of the image to be processed.
In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, will not be described again here.
Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-mentioned method. The computer readable storage medium may be a volatile or non-volatile computer readable storage medium.
An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.
The disclosed embodiments also provide a computer program product comprising computer readable code or a non-transitory computer readable storage medium carrying computer readable code, which when run in a processor of an electronic device, the processor in the electronic device performs the above method.
The electronic device may be provided as a terminal, server, or other form of device.
Fig. 3 illustrates a block diagram of an electronic device 800 in accordance with an embodiment of the disclosure. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like terminal.
Referring to fig. 3, electronic device 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.
The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.
The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 800 is in an operation mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the electronic device 800. For example, the sensor assembly 814 may detect an open/closed state of the electronic device 800, the relative positioning of components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, orientation or acceleration/deceleration of the electronic device 800, and a change in the temperature of the electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a Complementary Metal Oxide Semiconductor (CMOS) or Charge Coupled Device (CCD) image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 may access a wireless network based on a communication standard, such as a wireless network (WiFi), a second generation mobile communication technology (2G) or a third generation mobile communication technology (3G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the electronic device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the electronic device 800 to perform the above-described methods.
Fig. 4 shows a block diagram of an electronic device 1900 according to an embodiment of the disclosure. For example, the electronic device 1900 may be provided as a server. Referring to fig. 4, electronic device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.
The electronic device 1900 may also include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input/output (I/O) interface 1958. The electronic device 1900 may operate based on an operating system, such as the Microsoft Server operating system (Windows Server), stored in the memory 1932TM) Apple Inc. of the present application based on the graphic user interface operating System (Mac OS X)TM) Multi-user, multi-process computer operating system (Unix)TM) Free and open native code Unix-like operating System (Linux)TM) Open native code Unix-like operating System (FreeBSD)TM) Or the like.
In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the electronic device 1900 to perform the above-described methods.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: 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), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions 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 server. 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). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The computer program product may be embodied in hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied in a computer storage medium, and in another alternative embodiment, the computer program product is embodied in a Software product, such as a Software Development Kit (SDK), or the like.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. An image processing method, comprising:
determining linear estimation parameters of a plurality of image positions of an image to be processed according to the image to be processed and a guide image of the image to be processed;
obtaining a structural diagram of the image to be processed according to the guide image and the linear estimation parameters of the plurality of image positions;
fusing the structure chart with the texture chart of the image to be processed to obtain an image processing result of the image to be processed,
the linear estimation parameter of any image position in the image to be processed is associated with a region variance value and a region entropy value of a first image region in the guide image, and the first image region comprises an image region with a preset size and taking the image position as a center in the guide image.
2. The method of claim 1, wherein determining linear estimation parameters for a plurality of image positions of the image to be processed from the image to be processed and the guide image comprises:
aiming at any image position of the image to be processed, determining a first image area and a second image area corresponding to the image position, wherein the second image area comprises an image area which is centered at the image position and has a preset size in the image to be processed;
respectively determining the region parameters of the first image region and the second image region, wherein the region parameters comprise the region variance value, the region entropy value and the first region mean value of the pixel points in the first image region, the second region mean value of the pixel points in the second image region, and the third region mean value of the fusion region of the first image region and the second image region;
and determining a linear estimation parameter of the image position according to the region parameter.
3. The method of claim 2, wherein the determining the region parameters of the first image region and the second image region respectively comprises:
fusing the first image area and the second image area to obtain a fused area;
and determining the average value of the third area of the pixel points in the fusion area.
4. The method according to claim 2 or 3, wherein the determining the region parameters of the first image region and the second image region respectively comprises:
determining the occurrence probability of each pixel point in the first image area in the guide image according to the brightness histogram of the guide image;
and determining the region entropy value according to the occurrence probability of each pixel point in the first image region.
5. The method according to any of claims 2-4, wherein the linear estimation parameters comprise a first parameter and a second parameter,
the determining a linear estimation parameter of the image position according to the region parameter includes:
determining a first parameter of the image position according to the region variance value, the region entropy value, the first region mean value, the second region mean value and the third region mean value;
and determining a second parameter of the image position according to the first area mean value, the second area mean value and the first parameter.
6. The method according to any one of claims 1-5, further comprising:
and performing linear filtering on the image to be processed to obtain a guide image of the image to be processed.
7. The method according to any one of claims 1-6, further comprising:
and decomposing the image to be processed to obtain a texture map of the image to be processed.
8. An image processing apparatus characterized by comprising:
the parameter determining module is used for determining linear estimation parameters of a plurality of image positions of the image to be processed according to the image to be processed and the guide image of the image to be processed;
the structure chart acquisition module is used for acquiring a structure chart of the image to be processed according to the guide image and the linear estimation parameters of the plurality of image positions;
a result determining module, configured to fuse the structure diagram with the texture map of the image to be processed to obtain an image processing result of the image to be processed,
the linear estimation parameter of any image position in the image to be processed is associated with a region variance value and a region entropy value of a first image region in the guide image, and the first image region comprises an image region with a preset size and taking the image position as a center in the guide image.
9. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the memory-stored instructions to perform the method of any of claims 1 to 7.
10. A computer readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1 to 7.
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| PCT/CN2021/120905 WO2022227394A1 (en) | 2021-04-28 | 2021-09-27 | Image processing method and apparatus, and device, storage medium and program |
| KR1020227027309A KR20220149514A (en) | 2021-04-28 | 2021-09-27 | Image processing method, apparatus, apparatus, storage medium and program |
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