CN115423716A - Image enhancement method, device and equipment based on multidimensional filtering and storage medium - Google Patents
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Abstract
The invention relates to an image processing technology, and discloses an image enhancement method based on multi-dimensional filtering, which comprises the following steps: acquiring an image to be enhanced, and performing contrast correction on the image to be enhanced to obtain a first corrected image; carrying out color correction on the image to be enhanced to obtain a second correction image, and carrying out high-frequency filtering and low-frequency filtering on the second correction image to obtain a high-frequency filtering image and a low-frequency filtering image; performing superposition calculation on the high-frequency filtering image and the low-frequency filtering image to obtain a fusion image; carrying out multi-scale filtering and detail enhancement on the fused image to obtain a detail enhanced image; and carrying out multi-scale fusion on the image to be enhanced, the first correction image and the detail enhancement image to obtain an enhanced image. The invention also provides an image enhancement device based on the multidimensional filtering, electronic equipment and a storage medium. The invention can restore the image details after image enhancement and improve the image enhancement effect.
Description
Technical Field
The present invention relates to the field of image processing technologies, and in particular, to an image enhancement method and apparatus based on multidimensional filtering, an electronic device, and a computer-readable storage medium.
Background
Due to the defects of the sensor and the influence of insufficient light of the photographing environment, the collected image is often insufficient in illumination, low in contrast and hidden in important information. Image enhancement is essential in order to suitably improve the brightness and contrast of an image, sufficiently display information of the image. Image enhancement does not simply increase the brightness and contrast of an image, and over-enhancement can also destroy the information of the image. In the prior art, the histogram distribution of an image can reflect the quality of the image, so that the pixels of the image can be redistributed by an image enhancement method based on a histogram equalization technology, and the brightness and the contrast of the image are improved. Therefore, how to restore image details after image enhancement and improve the image enhancement effect becomes an urgent problem to be solved.
Disclosure of Invention
The invention provides an image enhancement method and device based on multi-dimensional filtering, electronic equipment and a computer readable storage medium, and mainly aims to solve the problems that image details cannot be fully restored after image enhancement and the image enhancement effect is poor.
In order to achieve the above object, the present invention provides an image enhancement method based on multi-dimensional filtering, including:
acquiring an image to be enhanced, and performing contrast correction on the image to be enhanced to obtain a first corrected image;
carrying out color correction on the image to be enhanced to obtain a second corrected image, and carrying out high-frequency filtering and low-frequency filtering on the second corrected image to obtain a high-frequency filtered image and a low-frequency filtered image;
performing superposition calculation on the high-frequency filtering image and the low-frequency filtering image to obtain a fusion image;
carrying out multi-scale filtering and detail enhancement on the fusion image to obtain a detail enhanced image;
and carrying out multi-scale fusion on the image to be enhanced, the first corrected image and the detail enhanced image to obtain an enhanced image.
Optionally, the performing contrast correction on the image to be enhanced to obtain a first corrected image includes:
dividing the image to be enhanced into a plurality of equidistant image blocks;
carrying out gray mapping on the equidistant image blocks to obtain a first corrected image;
performing gray mapping on the equidistant image blocks by using the following formula:
wherein, P is the first correction image, N =0,1, …, and N-1,M are the pixels of the equidistant image block; n is the number of gray levels of the gray mapping; q (k) is the k-th gray level pixel quantity of the equidistant image block.
Optionally, the color correcting the image to be enhanced to obtain a second corrected image includes:
recording the maximum value and the minimum value of the RGB channel in the image to be enhanced to obtain a bright part and a dark part;
and taking the area of the image to be enhanced except the bright part and the dark part as an area to be processed, and performing contrast stretching and affine transformation on the red, green and blue three channels of the area to be processed to obtain a second correction image.
Optionally, the performing high-frequency filtering and low-frequency filtering on the second correction image to obtain a high-frequency filtered image and a low-frequency filtered image includes:
determining a filtering center according to the second correction image, constructing a pixel coordinate system according to the second correction image, and calculating a distance value between a pixel coordinate point of each pixel in the pixel coordinate system and the filtering center;
acquiring a high-frequency increasing multiple and a low-frequency increasing multiple, and calculating according to the pixel coordinate point, the distance value, the high-frequency increasing multiple and the low-frequency increasing multiple to obtain high-frequency filtering data and high-frequency filtering data corresponding to each pixel;
calculating high-frequency filtering data and high-frequency filtering data corresponding to each pixel by using the following formula:
wherein H h (u, v) is high-frequency filtered data of a pixel corresponding to the pixel coordinate point (u, v); h l (u, v) is low frequency filtered data of a pixel corresponding to the pixel coordinate point (u, v); r is a radical of hydrogen H Increase the multiple for high frequency; r is L Increase by a factor of low frequency; c is a preset sharpening coefficient, r L ＜c＜r H ；D 2 (u, v) is a distance value of the pixel coordinate point (u, v) from the filter center; d 0 Is a preset cut-off frequency;
and summarizing the high-frequency filtering data and the high-frequency filtering data of each pixel to obtain a high-frequency filtering image and a low-frequency filtering image.
Optionally, the performing superposition calculation on the high-frequency filtering image and the low-frequency filtering image to obtain a fused image includes:
performing Fourier transform and inverse transform on the high-frequency filtering image and the low-frequency filtering image to obtain first transform data and second transform data;
linearly superposing high-frequency components and low-frequency components of the first transformation data and the second transformation data to obtain first linear data and second linear data;
and carrying out normalization processing on the first linear data and the second linear data to obtain a fused image.
Optionally, the performing multi-scale filtering and detail enhancement on the fused image to obtain a detail-enhanced image includes:
converting the fusion image into an HSV space to obtain an H-channel fusion image, an S-channel fusion image and a V-channel fusion image;
performing illumination estimation on the V-channel fusion image by using preset small-scale filtering to obtain a first illumination component and a first reflection component;
illumination estimation is performed on the V-channel fused image using the following formula:
F 1 ＝I V *ζ 1
G 1 ＝I V /F 1
wherein, F 1 Is the first illumination component; g 1 Is the first reflected component; zeta 1 Filtering the small scale; i is V Fusing images for the V channel;
carrying out filter decomposition on the first illumination component by utilizing preset mesoscale filtering to obtain a second illumination component and a second reflection component;
filter decomposing the first illumination component using:
F 2 ＝F 1 *ζ 2
G 2 ＝F 1 /F 2
wherein, F 2 Is the second illumination component; g 2 Is the second reflected component; zeta 2 Filtering the mesoscale; f 1 Is the first illumination component;
carrying out filter decomposition on the second illumination component by utilizing preset large-scale filtering to obtain a third illumination component and a third reflection component;
filter decomposing the first illumination component using:
F 3 ＝F 2 *ζ 3
G 3 ＝F 2 /F 3
wherein, F 3 Is the third illumination component; g 3 Is the third reflected component; zeta 3 Is the large scaleFiltering; f 2 Is the second illumination component;
performing brightness enhancement according to the first illumination component, the second illumination component and the third illumination component to obtain a brightness enhancement component;
performing luminance enhancement from the first, second, and third illumination components using:
U i ＝(F i ) γ
wherein F is the luminance enhancement component; f i Is the ith illumination component, and M is the total amount of the illumination components; gamma is more than 0 and less than 1;
performing detail fusion according to the first reflection component, the second reflection component and the third reflection component to obtain a detail enhancement component;
performing detail fusion from the first, second, and third reflection components using the following equation:
G＝(1-ω 1 ·sgn(G 1 ))×G 1 +ω 2 ×G 2 +(1-ω 2 )×G 3
wherein G is the detail enhancement component; g 1 Is the first reflected component; g 2 Is the second reflected component; g 3 Is the first reflected component; omega 1 、ω 2 Is a preset fusion weight factor;
and performing V-channel fusion according to the brightness enhancement component and the detail enhancement component to obtain a V-channel enhanced image, and converting the H-channel fused image, the S-channel fused image and the V-channel enhanced image into an RGB space to obtain a detail enhanced image.
Optionally, the performing multi-scale fusion on the image to be enhanced, the first corrected image, and the detail enhanced image to obtain an enhanced image includes:
acquiring a weight map corresponding to the image to be enhanced, the first correction image and the detail enhancement image;
performing laplacian decomposition on the image to be enhanced, the first corrected image and the detail enhanced image to obtain a first layer of laplacian pyramid corresponding to the image to be enhanced, a second layer of laplacian pyramid corresponding to the first corrected image and a third layer of laplacian pyramid corresponding to the detail enhanced image;
performing Gaussian decomposition on the weight map to obtain a first layer of Gaussian pyramid corresponding to the image to be enhanced, a second layer of Gaussian pyramid corresponding to the first corrected image and a third layer of Gaussian pyramid corresponding to the detail enhanced image;
constructing a Laplacian pyramid according to the first layer of Laplacian pyramid, the second layer of Laplacian pyramid and the third layer of Laplacian pyramid, and constructing a Gaussian pyramid according to the first layer of Gaussian pyramid, the second layer of Gaussian pyramid and the third layer of Gaussian pyramid;
and performing upsampling according to the Laplacian pyramid and the Gaussian pyramid to obtain an enhanced image.
In order to solve the above problem, the present invention further provides an image enhancement apparatus based on multi-dimensional filtering, the apparatus comprising:
the contrast correction module is used for acquiring an image to be enhanced and carrying out contrast correction on the image to be enhanced to obtain a first corrected image;
the filtering image generation module is used for carrying out color correction on the image to be enhanced to obtain a second correction image, and carrying out high-frequency filtering and low-frequency filtering on the second correction image to obtain a high-frequency filtering image and a low-frequency filtering image;
the fusion image generation module is used for carrying out superposition calculation on the high-frequency filtering image and the low-frequency filtering image to obtain a fusion image;
the detail enhancement image generation module is used for carrying out multi-scale filtering and detail enhancement on the fusion image to obtain a detail enhancement image;
and the enhanced image generation module is used for carrying out multi-scale fusion on the image to be enhanced, the first correction image and the detail enhanced image to obtain an enhanced image.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and (c) a second step of,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the multi-dimensional filtering based image enhancement method described above.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, the at least one computer program being executed by a processor in an electronic device to implement the multi-dimensional filtering-based image enhancement method described above.
According to the embodiment of the invention, the contrast correction is carried out on the image to be enhanced, so that the optimization of the image details is preliminarily realized; the color of the image to be enhanced is corrected, so that color distortion is eliminated, the color saturation of the image to be enhanced is restored, and the color of the image is restored more naturally; by carrying out low-frequency filtering and high-frequency filtering processing on the first corrected image and carrying out fusion calculation on the high-frequency filtered image and the low-frequency filtered image, the loss of image details is reduced, the contrast of the image can be enhanced, the brightness range of the image can be compressed, and the balance of the image brightness and the enhancement of the image detail information are realized; by carrying out multi-scale filtering and detail enhancement on the fused image, multi-scale details are used, the image enhancement quality is improved, and the image detail effect is enhanced; by carrying out multi-scale fusion on the image to be enhanced, the first correction image and the detail enhancement image, the halo phenomenon generated by a single-scale image is avoided, the enhanced image with the advantage of multi-scale is obtained, and the enhanced image with natural color, strong contrast and high definition is obtained. Therefore, the image enhancement method, the image enhancement device, the electronic equipment and the computer readable storage medium based on the multidimensional filtering can solve the problems that image details cannot be fully restored after image enhancement and the image enhancement effect is poor.
Drawings
Fig. 1 is a schematic flowchart of an image enhancement method based on multi-dimensional filtering according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating high-frequency filtering and low-frequency filtering performed on a second corrected image according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of a process of performing superposition calculation on a high-frequency filtered image and a low-frequency filtered image according to an embodiment of the present invention;
FIG. 4 is a functional block diagram of an image enhancement apparatus based on multi-dimensional filtering according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device for implementing the image enhancement method based on multi-dimensional filtering according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides an image enhancement method based on multi-dimensional filtering. The execution subject of the image enhancement method based on multi-dimensional filtering includes, but is not limited to, at least one of electronic devices such as a server and a terminal, which can be configured to execute the method provided by the embodiments of the present application. In other words, the image enhancement method based on multi-dimensional filtering may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Fig. 1 is a schematic flowchart of an image enhancement method based on multi-dimensional filtering according to an embodiment of the present invention. In this embodiment, the image enhancement method based on multi-dimensional filtering includes:
s1, obtaining an image to be enhanced, and carrying out contrast correction on the image to be enhanced to obtain a first corrected image.
In the embodiment of the invention, the image to be enhanced can be an infrared image shot under low brightness, an underwater image shot in different water depths, a moving image shot on a road and the like; compared with an image shot under a normal condition, the image to be enhanced has the conditions of low contrast and brightness, abnormal color, image real object boundary, image detail blurring and the like.
In the embodiment of the present invention, the performing contrast correction on the image to be enhanced to obtain a first corrected image includes:
dividing the image to be enhanced into a plurality of equidistant image blocks;
and carrying out gray mapping on the equidistant image blocks to obtain a first corrected image.
In detail, in the embodiment of the present invention, the equidistant image blocks are subjected to gray scale mapping by using the following formula:
wherein P is the first corrected image, N =0,1.., N-1,M is the pixels of the equidistant image block; n is the number of gray levels of the gray mapping; q (k) is the k-th gray level pixel quantity of the equidistant image block.
In the embodiment of the invention, the image detail information of the image to be enhanced can be optimized through contrast correction.
S2, performing color correction on the image to be enhanced to obtain a second corrected image, and performing high-frequency filtering and low-frequency filtering on the second corrected image to obtain a high-frequency filtered image and a low-frequency filtered image.
In this embodiment of the present invention, the performing color correction on the image to be enhanced to obtain a second corrected image includes:
recording the maximum value and the minimum value of the RGB channel in the image to be enhanced to obtain a bright part and a dark part;
and taking the area of the image to be enhanced except the bright part and the dark part as an area to be processed, and performing contrast stretching and affine transformation on the red, green and blue three channels of the area to be processed to obtain a second correction image.
In the embodiment of the invention, affine transformation is realized by linear transformation and translation through matrix multiplication and matrix addition, and affine transformation at low latitude can be realized by linear transformation at high latitude; the affine transformation maintains the "straightness" (straight lines remain straight lines after affine transformation) and the "parallelism" (relative positional relationship between straight lines remains unchanged, parallel lines remain parallel lines after affine transformation, and the positional order of points on the straight lines does not change) of the two-dimensional image.
Referring to fig. 2, in the embodiment of the present invention, the performing high-frequency filtering and low-frequency filtering on the second correction image to obtain a high-frequency filtering image and a low-frequency filtering image includes:
s21, determining a filtering center according to the second correction image, constructing a pixel coordinate system according to the second correction image, and calculating a distance value between a pixel coordinate point of each pixel in the pixel coordinate system and the filtering center;
s22, obtaining a high-frequency increasing multiple and a low-frequency increasing multiple, and calculating according to the pixel coordinate point, the distance value, the high-frequency increasing multiple and the low-frequency increasing multiple to obtain high-frequency filtering data and high-frequency filtering data corresponding to each pixel;
and S23, summarizing the high-frequency filtering data and the high-frequency filtering data of each pixel to obtain a high-frequency filtering image and a low-frequency filtering image.
In detail, in the embodiment of the present invention, the high-frequency filtered data and the high-frequency filtered data corresponding to each pixel may be calculated by using the following formula:
wherein H h (u, v) is high-frequency filtered data of a pixel corresponding to the pixel coordinate point (u, v); h l (u, v) is low frequency filtered data of a pixel corresponding to the pixel coordinate point (u, v); r is H Increase the high frequency by a factor; r is L Increase by a factor of low frequency; c is a preset sharpening coefficient, r L ＜c＜r H ；D 2 (u, v) is a distance value of the pixel coordinate point (u, v) from the filter center; d 0 Is a preset cut-off frequency.
In an optional embodiment of the present invention, c may take the value 3,r L The value can be 0.7 r H Can take the value of 5; since most of the edge (contour) information and detail information in the image exist in the high frequency part, the high frequency signal in the image needs to be strengthened, so that the contrast of the image can be enhanced; since the low-frequency component is a comprehensive measurement of the intensity of the whole image and describes the overall brightness change of the image in a large range, the low-frequency signal in the image needs to be suppressed, so that the overall change of the image is relatively uniform, and the dynamic range of the image can be compressed. Therefore, the high-frequency filtering and the low-frequency filtering can enhance the contrast of the image and compress the brightness range of the image so as to achieve the purposes of balancing the image brightness and enhancing the image detail information.
And S3, performing superposition calculation on the high-frequency filtering image and the low-frequency filtering image to obtain a fusion image.
Referring to fig. 3, in the embodiment of the present invention, the performing superposition calculation on the high-frequency filtering image and the low-frequency filtering image to obtain a fused image includes:
s31, performing Fourier transform and inverse transform on the high-frequency filtering image and the low-frequency filtering image to obtain first transform data and second transform data;
s32, linearly superposing high-frequency components and low-frequency components of the first conversion data and the second conversion data to obtain first linear data and second linear data;
s33, normalizing the first linear data and the second linear data to obtain a fusion image.
In the embodiment of the invention, the high-frequency filtering image and the low-frequency filtering image obtained by homomorphic filtering processing are subjected to fusion calculation, so that the brightness of the image to be enhanced is increased and the details are optimized.
And S4, carrying out multi-scale filtering and detail enhancement on the fusion image to obtain a detail enhanced image.
In the embodiment of the present invention, the performing multi-scale filtering and detail enhancement on the fusion image to obtain a detail enhanced image includes:
converting the fused image into an HSV space to obtain an H-channel fused image, an S-channel fused image and a V-channel fused image;
performing illumination estimation on the V-channel fusion image by using preset small-scale filtering to obtain a first illumination component and a first reflection component;
carrying out filter decomposition on the first illumination component by utilizing preset mesoscale filtering to obtain a second illumination component and a second reflection component;
carrying out filter decomposition on the second illumination component by utilizing preset large-scale filtering to obtain a third illumination component and a third reflection component;
performing brightness enhancement according to the first illumination component, the second illumination component and the third illumination component to obtain a brightness enhancement component;
performing detail fusion according to the first reflection component, the second reflection component and the third reflection component to obtain a detail enhancement component;
and performing V-channel fusion according to the brightness enhancement component and the detail enhancement component to obtain a V-channel enhanced image, and converting the H-channel fused image, the S-channel fused image and the V-channel enhanced image into an RGB space to obtain a detail enhanced image.
In detail, in the embodiment of the present invention, the illumination estimation may be performed on the V-channel fusion image by using the following formula:
F 1 ＝I V *ζ 1
G 1 ＝I V /F 1
wherein, F 1 Is the first illumination component; g 1 Is the first reflected component; zeta 1 Filtering the small scale; i is V Fusing images for the V channel;
the first illumination component may be filter decomposed using the following equation:
F 2 ＝F 1 *ζ 2
G 2 ＝F 1 /F 2
wherein, F 2 Is the second illumination component; g 2 Is the second reflected component; zeta 2 Filtering the mesoscale; f 1 Is the first illumination component;
the first illumination component may be filter decomposed using the following equation:
F 3 ＝F 2 *ζ 3
G 3 ＝F 2 /F 3
wherein, F 3 Is the third illumination component; g 3 Is the third reflected component; zeta 3 Filtering for the large scale; f 2 Is the second illumination component.
Specifically, in the embodiment of the present invention, the luminance enhancement may be performed according to the first illumination component, the second illumination component, and the third illumination component by using the following formula:
U i ＝(F i ) γ
wherein F is the luminance enhancement component; f i Is the ith illumination component, and M is the total amount of the illumination components; gamma is more than 0 and less than 1;
the detail fusion may be performed from the first, second, and third reflection components using the following equation:
G＝(1-ω 1 ·sgn(G 1 ))×G 1 +ω 2 ×G 2 +(1-ω 2 )×G 3
wherein G is the detail enhancement component; g 1 Is the first reflected component; g 2 Is the second reflected component; g 3 Is the first reflected component; omega 1 、ω 2 Is a preset fusion weight factor.
Further, in the embodiment of the present invention, V-channel fusion may be performed according to the luminance enhancement component and the detail enhancement component by using the following formula:
wherein Q is the V channel enhanced image; f is the luminance enhancement component; g is the detail enhancement component.
In the embodiment of the invention, the multiple brightness enhancement components and the multiple reflection components are calculated by utilizing the multi-scale filtering and detail enhancement method and are respectively subjected to component fusion, so that the detail effect of the image can be enhanced, and the image enhancement quality is improved
And S5, carrying out multi-scale fusion on the image to be enhanced, the first corrected image and the detail enhanced image to obtain an enhanced image.
In the embodiment of the present invention, the performing multi-scale fusion on the image to be enhanced, the first corrected image, and the detail enhanced image to obtain an enhanced image includes:
acquiring a weight map corresponding to the image to be enhanced, the first correction image and the detail enhancement image;
performing laplacian decomposition on the image to be enhanced, the first corrected image and the detail enhanced image to obtain a first layer of laplacian pyramid corresponding to the image to be enhanced, a second layer of laplacian pyramid corresponding to the first corrected image and a third layer of laplacian pyramid corresponding to the detail enhanced image;
performing Gaussian decomposition on the weight map to obtain a first layer of Gaussian pyramid corresponding to the image to be enhanced, a second layer of Gaussian pyramid corresponding to the first corrected image and a third layer of Gaussian pyramid corresponding to the detail enhanced image;
constructing a Laplacian pyramid according to the first layer of Laplacian pyramid, the second layer of Laplacian pyramid and the third layer of Laplacian pyramid, and constructing a Gaussian pyramid according to the first layer of Gaussian pyramid, the second layer of Gaussian pyramid and the third layer of Gaussian pyramid;
and performing upsampling according to the Laplacian pyramid and the Gaussian pyramid to obtain an enhanced image.
In the embodiment of the invention, the weight map can be composed of a laplacian contrast weight, a local contrast weight, a saliency weight and a saturation weight; and normalizing the Laplace contrast weight, the local contrast weight, the saliency weight and the saturation weight to obtain a weight map.
According to the embodiment of the invention, the contrast correction is carried out on the image to be enhanced, so that the optimization of the image details is preliminarily realized; the color correction is carried out on the image to be enhanced, so that the color distortion is eliminated, the color saturation of the image to be enhanced is recovered, and the image color is restored more naturally; by carrying out low-frequency filtering and high-frequency filtering processing on the first corrected image and carrying out fusion calculation on the high-frequency filtered image and the low-frequency filtered image, the loss of image details is reduced, the contrast of the image can be enhanced, the brightness range of the image can be compressed, and the balance of the image brightness and the enhancement of the image detail information are realized; by carrying out multi-scale filtering and detail enhancement on the fused image, multi-scale details are used, the image enhancement quality is improved, and the image detail effect is enhanced; by carrying out multi-scale fusion on the image to be enhanced, the first correction image and the detail enhancement image, the halo phenomenon generated by a single-scale image is avoided, the enhanced image with the advantage of multi-scale is obtained, and the enhanced image with natural color, strong contrast and high definition is obtained. Therefore, the image enhancement method based on the multidimensional filtering can solve the problems that the image details cannot be fully restored after the image enhancement and the image enhancement effect is poor.
Fig. 4 is a functional block diagram of an image enhancement apparatus based on multi-dimensional filtering according to an embodiment of the present invention.
The image enhancement apparatus 100 based on multi-dimensional filtering according to the present invention can be installed in an electronic device. According to the realized functions, the image enhancement device 100 based on multi-dimensional filtering can comprise a contrast correction module 101, a filtering image generation module 102, a fusion image generation module 103, a detail enhancement image generation module 104 and an enhancement image generation module 105. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the contrast correction module 101 is configured to obtain an image to be enhanced, and perform contrast correction on the image to be enhanced to obtain a first corrected image;
the filtered image generating module 102 is configured to perform color correction on the image to be enhanced to obtain a second corrected image, and perform high-frequency filtering and low-frequency filtering on the second corrected image to obtain a high-frequency filtered image and a low-frequency filtered image;
the fused image generation module 103 is configured to perform superposition calculation on the high-frequency filtered image and the low-frequency filtered image to obtain a fused image;
the detail enhancement image generation module 104 is configured to perform multi-scale filtering and detail enhancement on the fusion image to obtain a detail enhancement image;
the enhanced image generation module 105 is configured to perform multi-scale fusion on the image to be enhanced, the first corrected image, and the detail enhanced image to obtain an enhanced image.
In detail, when the modules in the image enhancement apparatus 100 based on multi-dimensional filtering according to the embodiment of the present invention are used, the same technical means as the image enhancement method based on multi-dimensional filtering described in the drawings can be adopted, and the same technical effects can be produced, which is not described herein again.
Fig. 5 is a schematic structural diagram of an electronic device for implementing an image enhancement method based on multi-dimensional filtering according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as an image enhancement program based on multi-dimensional filtering, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (e.g., executing an image enhancement program based on multi-dimensional filtering, etc.) stored in the memory 11 and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only to store application software installed in the electronic device and various types of data, such as codes of an image enhancement program based on multi-dimensional filtering, etc., but also to temporarily store data that has been output or is to be output.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are commonly used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Fig. 5 only shows an electronic device with components, and it will be understood by a person skilled in the art that the structure shown in fig. 5 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The multidimensional filtering based image enhancement program stored in the memory 11 of the electronic device 1 is a combination of instructions that, when executed in the processor 10, enable:
acquiring an image to be enhanced, and performing contrast correction on the image to be enhanced to obtain a first corrected image;
carrying out color correction on the image to be enhanced to obtain a second correction image, and carrying out high-frequency filtering and low-frequency filtering on the second correction image to obtain a high-frequency filtering image and a low-frequency filtering image;
performing superposition calculation on the high-frequency filtering image and the low-frequency filtering image to obtain a fusion image;
carrying out multi-scale filtering and detail enhancement on the fused image to obtain a detail enhanced image;
and carrying out multi-scale fusion on the image to be enhanced, the first correction image and the detail enhancement image to obtain an enhanced image.
Specifically, the specific implementation method of the instruction by the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to the drawings, which is not described herein again.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring an image to be enhanced, and performing contrast correction on the image to be enhanced to obtain a first corrected image;
carrying out color correction on the image to be enhanced to obtain a second correction image, and carrying out high-frequency filtering and low-frequency filtering on the second correction image to obtain a high-frequency filtering image and a low-frequency filtering image;
performing superposition calculation on the high-frequency filtering image and the low-frequency filtering image to obtain a fusion image;
carrying out multi-scale filtering and detail enhancement on the fused image to obtain a detail enhanced image;
and carrying out multi-scale fusion on the image to be enhanced, the first correction image and the detail enhancement image to obtain an enhanced image.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. An image enhancement method based on multi-dimensional filtering, characterized in that the method comprises:
acquiring an image to be enhanced, and performing contrast correction on the image to be enhanced to obtain a first corrected image;
carrying out color correction on the image to be enhanced to obtain a second correction image, and carrying out high-frequency filtering and low-frequency filtering on the second correction image to obtain a high-frequency filtering image and a low-frequency filtering image;
performing superposition calculation on the high-frequency filtering image and the low-frequency filtering image to obtain a fusion image;
carrying out multi-scale filtering and detail enhancement on the fusion image to obtain a detail enhanced image;
and carrying out multi-scale fusion on the image to be enhanced, the first correction image and the detail enhancement image to obtain an enhanced image.
2. The image enhancement method based on multi-dimensional filtering as claimed in claim 1, wherein said performing contrast correction on the image to be enhanced to obtain a first corrected image comprises:
dividing the image to be enhanced into a plurality of equidistant image blocks;
carrying out gray mapping on the equidistant image blocks to obtain a first corrected image;
performing gray mapping on the equidistant image blocks by using the following formula:
wherein, P is the first correction image, N =0,1, …, and N-1,M are the pixels of the equidistant image block; n is the number of gray levels of the gray mapping; q (k) is the k-th gray level pixel quantity of the equidistant image block.
3. The method for image enhancement based on multi-dimensional filtering as claimed in claim 1, wherein said color correcting said image to be enhanced to obtain a second corrected image comprises:
recording the maximum value and the minimum value of the RGB channel in the image to be enhanced to obtain a bright part and a dark part;
and taking the area of the image to be enhanced except the bright part and the dark part as an area to be processed, and performing contrast stretching and affine transformation on the red, green and blue three channels of the area to be processed to obtain a second correction image.
4. The method for image enhancement based on multi-dimensional filtering according to claim 1, wherein said performing high-frequency filtering and low-frequency filtering on the second corrected image to obtain a high-frequency filtered image and a low-frequency filtered image comprises:
determining a filtering center according to the second correction image, constructing a pixel coordinate system according to the second correction image, and calculating a distance value between a pixel coordinate point of each pixel in the pixel coordinate system and the filtering center;
acquiring a high-frequency increasing multiple and a low-frequency increasing multiple, and calculating according to the pixel coordinate point, the distance value, the high-frequency increasing multiple and the low-frequency increasing multiple to obtain high-frequency filtering data and high-frequency filtering data corresponding to each pixel;
calculating high-frequency filtering data and high-frequency filtering data corresponding to each pixel by using the following formula:
wherein H h (u, v) is high-frequency filtered data of a pixel corresponding to the pixel coordinate point (u, v); h l (u, v) is low frequency filtered data of a pixel corresponding to the pixel coordinate point (u, v); r is H Increase the high frequency by a factor; r is L Increase by a factor of low frequency; c is a preset sharpening coefficient, r L <c<r H ；D 2 (u, v) is a distance value of the pixel coordinate point (u, v) from the filter center; d 0 A preset cut-off frequency;
and summarizing the high-frequency filtering data and the high-frequency filtering data of each pixel to obtain a high-frequency filtering image and a low-frequency filtering image.
5. The method for enhancing images based on multidimensional filtering of claim 1, wherein the step of performing the superposition calculation on the high-frequency filtered image and the low-frequency filtered image to obtain a fused image comprises:
carrying out Fourier transform and inverse transform on the high-frequency filtering image and the low-frequency filtering image to obtain first transform data and second transform data;
linearly superposing high-frequency components and low-frequency components of the first transformation data and the second transformation data to obtain first linear data and second linear data;
and carrying out normalization processing on the first linear data and the second linear data to obtain a fused image.
6. The image enhancement method based on multi-dimensional filtering as claimed in claim 1, wherein the multi-scale filtering and detail enhancement of the fused image to obtain a detail enhanced image comprises:
converting the fused image into an HSV space to obtain an H-channel fused image, an S-channel fused image and a V-channel fused image;
performing illumination estimation on the V-channel fusion image by using preset small-scale filtering to obtain a first illumination component and a first reflection component;
illumination estimation is performed on the V-channel fused image using the following formula:
F 1 ＝I V *ζ 1
G 1 ＝I V /E 1
wherein, F 1 Is the first illumination component; g 1 Is the first reflected component; ζ represents a unit 1 Filtering the small scale; i is V Fusing images for the V channel;
carrying out filter decomposition on the first illumination component by utilizing preset mesoscale filtering to obtain a second illumination component and a second reflection component;
filter decomposing the first illumination component using:
F 2 ＝F 1 *ζ 2
G 2 ＝F 1 /F 2
wherein, F 2 Is the second illumination component; g 2 Is the second reflected component; zeta 2 Filtering the mesoscale; f 1 Is the first illumination component;
performing filter decomposition on the second illumination component by using preset large-scale filtering to obtain a third illumination component and a third reflection component;
filter decomposing the first illumination component using:
F 3 ＝F 2 *ζ 3
G 3 ＝F 2 /F 3
wherein, F 3 Is the third illumination component; g 3 Is the third reflected component; zeta 3 Filtering for the large scale; f 2 Is the second illumination component;
performing brightness enhancement according to the first illumination component, the second illumination component and the third illumination component to obtain a brightness enhancement component;
performing luminance enhancement from the first, second, and third illumination components using:
U i ＝(F i ) γ
wherein F is the luminance enhancement component; f i Is the ith illumination component, and M is the total amount of the illumination components; 0<γ<1；
Performing detail fusion according to the first reflection component, the second reflection component and the third reflection component to obtain a detail enhancement component;
performing detail fusion from the first, second, and third reflection components using the following equation:
G＝(1-ω 1 ·sgn(G 1 ))×G 1 +ω 2 ×G 2 +(1-ω 2 )×G 3
wherein G is the detail enhancement component; g 1 Is the first reflected component; g 2 Is the second reflected component; g 3 Is the first reflected component; omega 1 、ω 2 Is a preset fusion weight factor;
and performing V-channel fusion according to the brightness enhancement component and the detail enhancement component to obtain a V-channel enhanced image, and converting the H-channel fused image, the S-channel fused image and the V-channel enhanced image into an RGB space to obtain a detail enhanced image.
7. The image enhancement method based on multi-dimensional filtering as claimed in any one of claims 1 to 6, wherein the multi-scale fusion of the image to be enhanced, the first corrected image and the detail enhanced image to obtain an enhanced image comprises:
acquiring a weight map corresponding to the image to be enhanced, the first correction image and the detail enhancement image;
performing laplacian decomposition on the image to be enhanced, the first corrected image and the detail enhanced image to obtain a first layer of laplacian pyramid corresponding to the image to be enhanced, a second layer of laplacian pyramid corresponding to the first corrected image and a third layer of laplacian pyramid corresponding to the detail enhanced image;
performing Gaussian decomposition on the weight map to obtain a first layer of Gaussian pyramid corresponding to the image to be enhanced, a second layer of Gaussian pyramid corresponding to the first corrected image and a third layer of Gaussian pyramid corresponding to the detail enhanced image;
constructing a Laplacian pyramid according to the first layer of Laplacian pyramid, the second layer of Laplacian pyramid and the third layer of Laplacian pyramid, and constructing a Gaussian pyramid according to the first layer of Gaussian pyramid, the second layer of Gaussian pyramid and the third layer of Gaussian pyramid;
and performing upsampling according to the Laplacian pyramid and the Gaussian pyramid to obtain an enhanced image.
8. An apparatus for image enhancement based on multi-dimensional filtering, the apparatus comprising:
the contrast correction module is used for acquiring an image to be enhanced and carrying out contrast correction on the image to be enhanced to obtain a first corrected image;
the filtering image generation module is used for carrying out color correction on the image to be enhanced to obtain a second correction image, and carrying out high-frequency filtering and low-frequency filtering on the second correction image to obtain a high-frequency filtering image and a low-frequency filtering image;
the fusion image generation module is used for carrying out superposition calculation on the high-frequency filtering image and the low-frequency filtering image to obtain a fusion image;
the detail enhancement image generation module is used for carrying out multi-scale filtering and detail enhancement on the fusion image to obtain a detail enhancement image;
and the enhanced image generation module is used for carrying out multi-scale fusion on the image to be enhanced, the first correction image and the detail enhanced image to obtain an enhanced image.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of image enhancement based on multi-dimensional filtering according to any one of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out a method for image enhancement based on multi-dimensional filtering according to any one of claims 1 to 7.
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