CN113409201B - Image enhancement processing method, device, equipment and medium - Google Patents
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Abstract
The invention discloses an image enhancement processing method, device, equipment and medium, wherein the method comprises the following steps: processing a first image input by a user according to a channelization processing rule to obtain a multi-channel characteristic image, extracting channel image information of each channel from the multi-channel characteristic image, respectively performing whitening processing on the channel characteristic image of the corresponding channel according to the channel image information to obtain a whitened characteristic image, performing cross-channel extraction to obtain cross-channel image information, respectively performing pixel-by-pixel optimization processing on the whitened characteristic image of each channel to obtain a corresponding optimized characteristic image, performing deconvolution processing to obtain a deconvolution image, and performing size adjustment on the deconvolution image to obtain a target optimized image. The invention belongs to the technical field of image processing, and particularly relates to a method for processing a channel characteristic image, which comprises the steps of respectively whitening each channel characteristic image according to channel image information obtained from first image information, and respectively enhancing each pixel in the image in a targeted manner, so that the effect of image enhancement is greatly enhanced.
Description
Technical Field
The invention relates to the technical field of image processing, belongs to an application scene for intelligently enhancing image quality in a smart city, and particularly relates to an image enhancement processing method, device, equipment and medium.
Background
The underwater image recognition plays an important role in the fields of marine research, underwater robots and the like. For example, the proper operation of an underwater monitoring system or an underwater unmanned device relies heavily on efficient image recognition. Research on marine ecology by marine biologists also requires clear images as support. However, the existing underwater image technology has a plurality of problems that an accurate and clear image cannot be shot in a visible spectrum, so that more accurate information can be acquired from the image shot under water, and the image shot under water can be subjected to image enhancement processing so as to acquire more accurate information from the image subjected to the enhancement processing. However, the processing mode of enhancing the image in the prior art method is limited, and the overall enhancement processing is performed on the image in the prior art method, so that the enhancement processing cannot be performed on a specific area in the image, and the quality of the enhanced image obtained after the enhancement processing is low, and the enhancement processing effect is poor. Therefore, the conventional method has a problem that the processing effect of the enhancement processing of the image is poor.
Disclosure of Invention
The embodiment of the invention provides an image enhancement processing method, device, equipment and medium, which aim to solve the problem of poor processing effect in the prior art method for enhancing images.
In a first aspect, an embodiment of the present invention provides an image enhancement processing method, including:
if a first image input by a user is received, processing the first image according to a preset channelized processing rule to obtain a corresponding multi-channel characteristic image;
extracting channel image information corresponding to the channel characteristic image of each channel from the multi-channel characteristic image according to a preset image information extraction rule;
respectively performing whitening treatment on channel characteristic images corresponding to the channel image information according to the channel image information of each channel to obtain whitened characteristic images corresponding to each channel;
performing cross-channel extraction on the whitened characteristic image to obtain corresponding cross-channel image information;
respectively carrying out pixel-by-pixel optimization on the whitened characteristic images of each channel according to a preset optimization model and the cross-channel image information to obtain optimized characteristic images corresponding to each channel;
deconvolution processing is carried out on the optimized feature image according to a preset deconvolution processing rule to obtain a corresponding deconvolution image;
and performing size adjustment on the deconvolution image according to the image size of the first image, and taking the obtained enhanced image as a target optimized image corresponding to the first image.
In a second aspect, an embodiment of the present invention provides an image enhancement processing apparatus, including:
the multi-channel characteristic image acquisition unit is used for processing the first image according to a preset channeling processing rule to obtain a corresponding multi-channel characteristic image if the first image input by a user is received;
a channel image information acquisition unit, configured to extract channel image information corresponding to a channel feature image of each channel from the multi-channel feature image according to a preset image information extraction rule;
the device comprises a whitening characteristic image acquisition unit, a processing unit and a processing unit, wherein the whitening characteristic image acquisition unit is used for respectively carrying out whitening processing on a channel characteristic image corresponding to the channel image information according to the channel image information of each channel to obtain a whitening characteristic image corresponding to each channel;
the cross-channel extraction unit is used for carrying out cross-channel extraction on the whitening characteristic image to obtain corresponding cross-channel image information;
the optimized characteristic image acquisition unit is used for respectively carrying out pixel-by-pixel optimization processing on the whitened characteristic image of each channel according to a preset optimized model and the cross-channel image information to obtain an optimized characteristic image corresponding to each channel;
The deconvolution image acquisition unit is used for carrying out deconvolution processing on the optimized characteristic image according to a preset deconvolution processing rule to obtain a corresponding deconvolution image;
and the target optimized image acquisition unit is used for carrying out size adjustment on the deconvolution image according to the image size of the first image, and taking the obtained enhanced image as a target optimized image corresponding to the first image.
In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor executes the computer program to implement the image enhancement processing method described in the first aspect.
In a fourth aspect, an embodiment of the present invention further provides a computer readable storage medium, where the computer readable storage medium stores a computer program, where the computer program when executed by a processor causes the processor to perform the image enhancement processing method described in the first aspect.
The embodiment of the invention provides an image enhancement processing method, an image enhancement processing device and a computer readable storage medium. Processing a first image input by a user according to a channelization processing rule to obtain a multi-channel characteristic image, extracting channel image information of each channel from the multi-channel characteristic image, respectively performing whitening processing on the channel characteristic image of the corresponding channel according to the channel image information to obtain a whitened characteristic image, performing cross-channel extraction to obtain cross-channel image information, respectively performing optimization processing on the whitened characteristic image of each channel to obtain a corresponding optimized characteristic image, performing deconvolution processing to obtain a deconvolution image, and performing size adjustment on the deconvolution image to obtain a target optimized image. According to the method, whitening processing is respectively carried out on each channel characteristic image according to the channel image information obtained from the first image information, then pixel-by-pixel optimization processing is carried out on the whitened characteristic images of each channel through the cross-channel image information, and each pixel in the image can be respectively enhanced in a targeted manner, so that the effect of enhancing the image is greatly enhanced, intelligent enhancement of the image quality is realized, and the image quality is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of an image enhancement processing method according to an embodiment of the present invention;
fig. 2 is a schematic sub-flowchart of an image enhancement processing method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of another sub-flowchart of an image enhancement processing method according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of another sub-flowchart of an image enhancement processing method according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of another sub-flowchart of an image enhancement processing method according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of another sub-flowchart of an image enhancement processing method according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of another sub-flowchart of an image enhancement processing method according to an embodiment of the present invention;
fig. 8 is a schematic block diagram of an image enhancement processing apparatus according to an embodiment of the present invention;
Fig. 9 is a schematic block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Referring to fig. 1, fig. 1 is a flowchart of an image enhancement processing method according to an embodiment of the present invention; the image enhancement processing method is applied to a user terminal or a management server, the image enhancement processing method is executed through application software installed in the user terminal or the management server, the user terminal is a terminal device which can receive a first image input by a user and perform image enhancement processing, such as a desktop computer, a notebook computer, a tablet computer or a mobile phone, and the management server is a server which can receive the first image sent by the user through the terminal and perform image enhancement processing, such as a server constructed by an enterprise, a medical institution or a government department. As shown in fig. 1, the method includes steps S110 to S170.
S110, if a first image input by a user is received, processing the first image according to a preset channeling processing rule to obtain a corresponding multi-channel characteristic image.
And if a first image input by a user is received, processing the first image according to a preset channelized processing rule to obtain a corresponding multi-channel characteristic image. The first image input by the user may be an image of poor image quality, such as an image taken in an environment of poor light, an image taken underwater, or the like. The channeling processing rule is a specific rule for processing the first image to obtain channel characteristic images corresponding to a plurality of channels, wherein the channeling processing rule comprises size information and a multi-channel convolution kernel, the size of the first image can be adjusted through the size information to obtain a second image matched with the size information, and the second image is subjected to convolution processing through multi-channel convolution check to obtain the corresponding multi-channel characteristic image.
In one embodiment, as shown in FIG. 2, step S110 includes sub-steps S111 and S112.
And S111, carrying out size adjustment on the first image according to the size information to obtain a second image matched with the size information.
Specifically, the first image may be downsampled according to the size information, and downsampled to reduce the first image, and then the first image may be reduced to a second image according to the size information, where the image size of the second image matches the size information. The size information may include information of an image length size, that is, information of the number of pixels included in the image in the length direction, and an image width size, that is, information of the number of pixels included in the image in the width direction. For example, the size information of the first image may be represented as nxm, and the size information in the channeling rule is nxm (N < N and M < M), and then the first image may be downsampled to obtain the second image with the size of nxm.
And S112, performing convolution processing on the second image according to the multi-channel convolution check to obtain a corresponding multi-channel characteristic image.
The multi-channel convolution kernel comprises a plurality of channels, each channel correspondingly comprises a convolution kernel or a plurality of convolution kernels, the convolution kernel of any one channel can obtain a channel characteristic image corresponding to the channel when carrying out convolution processing on the second image, the convolution kernels of the plurality of channels respectively carry out convolution processing on the second image to obtain a plurality of channel characteristic images respectively corresponding to the plurality of channels, and the plurality of channel characteristic images are formed into the multi-channel characteristic image corresponding to the second image. For example, if a channel includes two 3×3 convolution kernels, a convolution image can be obtained by performing a convolution process with a step length of 1 on a first convolution check second image, and then a channel feature image corresponding to the second image can be obtained by performing a further convolution process on the second convolution check convolution image, where the sizes of the obtained channel feature images are the same.
S120, extracting channel image information corresponding to the channel characteristic image of each channel from the multi-channel characteristic image according to a preset image information extraction rule.
And extracting channel image information corresponding to the channel characteristic image of each channel from the multi-channel characteristic image according to a preset image information extraction rule. The multi-channel characteristic image comprises channel characteristic images corresponding to each channel, channel image information can be correspondingly extracted from each channel characteristic image according to an image information extraction rule, the channel image information can characterize the whole information of the channel characteristic images, the image information extraction rule is a specific rule for extracting the channel image information from each channel characteristic image, wherein the image information extraction rule comprises a mean value calculation formula and a matrix calculation formula, and the channel image information comprises a mean value vector and a covariance matrix. The mean value vector of each channel characteristic image can be obtained through mean value calculation formula, and the covariance matrix of each channel characteristic image can be obtained through matrix calculation formula and mean value vector further calculation, wherein the mean value vector and covariance matrix of the channel characteristic image are combined to serve as channel image information of the channel characteristic image.
In one embodiment, as shown in FIG. 3, step S120 includes sub-steps S121 and S122.
S121, calculating the mean vector of each channel characteristic image according to the mean calculation formula.
In particular, the multi-channel feature image obtained from the second image may be represented as R C×H×W Any one pixel x in the multi-channel feature image can be expressed as x e R C×H×W Where C is the total number of channels, H is the length of the channel feature image, and W is the width of the channel feature image. And calculating one channel characteristic image according to the mean value calculation formula to obtain one mean value vector corresponding to the channel characteristic image, and calculating a plurality of channel characteristic images to obtain a plurality of corresponding mean value vectors.
The mean value calculation formulas can be expressed by adopting a formula (1):
wherein x is hw Namely, the pixel value corresponding to the pixel point with the coordinate position of (h, w) in a channel characteristic image is expressed, wherein h can be 1, H]Within any integer, w may be [1, W ]]Any integer, muThe mean vector obtained by calculation is obtained.
S122, calculating covariance matrixes of the characteristic images of each channel according to the matrix calculation formula and the mean value vector.
The covariance matrix of each channel feature image can be further calculated by combining the calculated mean vector through a matrix calculation formula. Specifically, the matrix calculation formula may be expressed by formula (2):
Wherein x is hw The method is characterized in that the method is expressed as pixel values corresponding to pixel points with coordinates of (h, w) in a channel characteristic image, mu is an average value vector corresponding to the channel characteristic image, T is matrix transformation calculation, alpha is a preset parameter value in a formula, alpha can be a positive integer, I is a unit matrix in the formula, and ψ is a covariance matrix corresponding to the channel characteristic image obtained through calculation.
S130, respectively performing whitening treatment on the channel characteristic images corresponding to the channel image information according to the channel image information of each channel to obtain whitened characteristic images corresponding to each channel.
And respectively performing whitening treatment on the channel characteristic images corresponding to the channel image information according to the channel image information of each channel to obtain whitening characteristic images corresponding to each channel. Each channel corresponds to one channel characteristic image, channel image information can be correspondingly obtained from each channel characteristic image, one channel corresponds to one piece of channel image information, whitening treatment can be carried out on the channel characteristic image uniquely corresponding to the channel image information according to each channel image information, a whitened characteristic image corresponding to each channel characteristic image is obtained, and then one channel corresponds to one whitened characteristic image.
In one embodiment, as shown in FIG. 4, step S130 includes sub-steps S131 and S132.
S131, calculating pixel difference values between each pixel value in each channel characteristic image and the mean value vector of the channel characteristic image respectively, and obtaining difference value information corresponding to each channel characteristic image.
Specifically, for any channel feature image, the pixel difference between the pixel value of each pixel point in the channel feature image and the mean vector of the channel feature image can be calculated, and the pixel difference of each pixel in the channel feature image is combined to obtain corresponding difference information, so that one channel feature image can correspondingly obtain one difference information.
S132, carrying out inverse transformation on each covariance matrix and multiplying each pixel difference value in the difference value information of the corresponding channel characteristic image respectively to obtain a whitened characteristic image corresponding to each channel characteristic image.
And carrying out inverse transformation on each covariance matrix, and multiplying the matrix obtained by the inverse transformation by each pixel difference value in the difference information of the corresponding channel characteristic image respectively, so that each pixel of the corresponding channel characteristic image can obtain a corresponding product result, and the product result corresponding to each pixel can be obtained to serve as a whitening characteristic image of the corresponding channel characteristic image.
Specifically, the above calculation process can be expressed by the formula (3):
Γ(x hw )＝Ψ -1/2 ×(x hw -μ) (3)；
wherein h is E [1, H]And is an integer, w is [1, W ]]And is an integer, ψ -1/2 Namely, the calculation process of inverting the covariance matrix psi of a certain channel characteristic image is that x hw Namely, the pixel value corresponding to the pixel point with the coordinate position of (h, w) in the difference information of the channel characteristic image is that μ is the mean vector corresponding to the channel characteristic image, Γ (x) hw ) I.e. the obtained sum x hw A corresponding one of the product results.
S140, performing cross-channel extraction on the whitened characteristic image to obtain corresponding cross-channel image information.
And carrying out cross-channel extraction on the whitened characteristic image to obtain corresponding cross-channel image information. Each channel corresponds to one whitening characteristic image, in order to obtain cross-channel information of multiple whitening characteristic images corresponding to multiple channels, cross-channel extraction can be performed on the multiple whitening characteristic images to obtain corresponding cross-channel image information, and because the cross-channel image information synthesizes the information of each whitening characteristic image, the cross-channel image information can perform integral characterization on the cross-channel information of the multiple whitening characteristic images. The cross-channel image information comprises a cross-channel mean value and a cross-channel standard deviation.
In one embodiment, as shown in FIG. 5, step S140 includes sub-steps S141 and S142.
S141, performing cross-channel average calculation on a plurality of pixel values corresponding to each pixel in the whitening characteristic image to obtain a cross-channel average value corresponding to each pixel.
Any pixel corresponds to a pixel value in each whitening characteristic image, and then one pixel corresponds to a plurality of pixel values in a plurality of whitening characteristic images, so that a plurality of pixel values corresponding to each pixel in the plurality of whitening characteristic images can be obtained to perform cross-channel average calculation, and a cross-channel average value corresponding to each pixel is obtained, namely, one pixel corresponds to one cross-channel average value only. Specifically, the calculation formula of the cross-channel average calculation can be expressed by using formula (4):
wherein h is E [1, H]And is an integer, w is [1, W ]]And is an integer, c.epsilon.1, C]And is an integer, y chw Namely, the pixel value corresponding to the pixel point with the coordinate position (h, w) in the whitened feature image corresponding to the C-th channel is the pixel value corresponding to the pixel point with the coordinate position (h, w), C represents the total number of channels, and ζ hw The cross-channel mean value corresponding to the pixel point with the coordinate position (h, w) obtained through calculation is obtained.
S142, calculating the cross-channel standard deviation of each pixel according to a preset standard deviation calculation formula and the cross-channel mean value.
The cross-channel standard deviation of each pixel can be calculated according to a standard deviation calculation formula by combining the obtained cross-channel mean values, and then one cross-channel standard deviation can be calculated by combining one pixel with one cross-channel mean value corresponding to the pixel. Specifically, the standard deviation calculation formula may be expressed by the formula (5):
wherein h is E [1, H]And is an integer, w is [1, W ]]And is an integer, c.epsilon.1, C]And is an integer, y chw Namely, the pixel value corresponding to the pixel point with the coordinate position (h, w) in the whitened feature image corresponding to the C-th channel is the pixel value corresponding to the pixel point with the coordinate position (h, w), C represents the total number of channels, and ζ hw Namely the cross-channel mean value sigma corresponding to the pixel point with the coordinate position (h, w) obtained by calculation hw The standard deviation of the cross channel corresponding to the pixel point with the coordinate position (h, w) obtained by calculation is shown as a preset parameter value in the formula, alpha' can be a positive integer, and I is an identity matrix in the formula.
And S150, respectively carrying out pixel-by-pixel optimization processing on the whitened characteristic images of each channel according to a preset optimization model and the cross-channel image information to obtain optimized characteristic images corresponding to each channel.
And respectively carrying out pixel-by-pixel optimization processing on the whitened characteristic images of each channel according to a preset optimization model and the cross-channel image information to obtain optimized characteristic images corresponding to each channel. The whitening characteristic images corresponding to each channel can be subjected to pixel-by-pixel optimization processing through an optimization model and the obtained cross-channel image information, namely each pixel in the whitening characteristic images is subjected to targeted optimization processing, a corresponding one piece of optimization characteristic image can be obtained through pixel-by-pixel optimization processing of one piece of whitening characteristic image, and a plurality of pieces of optimization characteristic images are obtained after pixel-by-pixel optimization processing of a plurality of kinds of whitening characteristic images, wherein the optimization model comprises a standardized calculation formula and a convolution operator.
In one embodiment, as shown in FIG. 6, step S150 includes sub-steps S151, S152, and S153.
And S151, respectively carrying out standardized calculation on each pixel value of each whitening characteristic image according to the standardized calculation formula and the cross-channel image information to obtain a standard pixel value of each pixel in each whitening characteristic image.
The standard pixel value corresponding to each pixel in each whitening characteristic image can be obtained by respectively carrying out standardized calculation on the pixel value of each pixel in each whitening characteristic image through a standardized calculation formula and the cross-channel mean value of each pixel in cross-channel image information. Specifically, the normalized calculation formula may be expressed by the formula (6):
wherein h is E [1, H]And is an integer, w is [1, W ]]And is an integer, c.epsilon.1, C]And is an integer, y chw Namely, the pixel value corresponding to the pixel point with the coordinate position (h, w) in the whitened feature image corresponding to the c-th channel is xi hw Namely, the cross-channel mean value and sigma corresponding to the pixel point with the coordinate position of (h, w) in the cross-channel image information hw Namely, the cross-channel standard deviation omega (y) corresponding to the pixel point with the coordinate position of (h, w) in the cross-channel image information chw ) I.e. calculated and y chw Corresponding standard pixel values.
And S152, performing convolution dimension reduction calculation on the cross-channel image information according to the convolution operator to obtain dimension reduced cross-channel image information.
The cross-channel mean and the cross-channel standard deviation contained in the cross-channel image information can be respectively subjected to convolution dimension reduction calculation according to a convolution operator, for example, the dimension reduction calculation can be performed through a 1*1 convolution kernel, and two different convolution kernels are used for respectively performing the convolution dimension reduction calculation on the cross-channel mean and the cross-channel standard deviation.
And S153, carrying out superposition calculation on the standard pixel value of each pixel in each whitening characteristic image and the cross-channel image information after dimension reduction to obtain an optimized characteristic image corresponding to each whitening characteristic image.
Specifically, the process of superposition calculation can be represented by equation (7):
z chw ＝F 1 (σ hw )×Ω(y chw )+F 2 (ξ hw ) (7)；
wherein h is E [1, H]And is an integer, w is [1, W ]]And is an integer, c.epsilon.1, C]And is an integer, Ω (y) chw ) Namely, the standard pixel value corresponding to the pixel point with the coordinate position of (h, w) in the whitened feature image corresponding to the c-th channel is F 1 (σ hw ) Namely, the calculated value obtained by dimension reduction calculation of the cross-channel standard deviation corresponding to the pixel point with the coordinate position of (h, w) is F 2 (ξ hw ) Namely, the calculated value obtained by performing dimension reduction calculation on the cross-channel mean value corresponding to the pixel point with the coordinate position of (h, w), and z chw Namely, is the pixel point y chw Corresponding superposition calculations.
And acquiring a superposition calculated value corresponding to each pixel point in any whitening characteristic image to be combined into an optimized characteristic image corresponding to the whitening characteristic image, and acquiring the optimized characteristic image corresponding to each whitening characteristic image according to the method.
And S160, deconvoluting the optimized feature image according to a preset deconvolution processing rule to obtain a corresponding deconvolution image.
And deconvoluting the optimized feature image according to a preset deconvolution processing rule to obtain a corresponding deconvolution image. Specifically, deconvolution processing is performed on the obtained plurality of optimized feature images, that is, inverse operation corresponding to convolution processing is performed on the second image, and the plurality of optimized feature images corresponding to the plurality of channels can be integrated into one deconvolution image through deconvolution operation, so that the deconvolution image contains image information obtained by combining the optimized feature images of each channel.
If the size of the optimized feature image is h×w and the number of the optimized feature images is C, the size of one deconvoluted image obtained by deconvolving the plurality of optimized feature images may be n×m.
And S170, carrying out size adjustment on the deconvolution image according to the image size of the first image, and taking the obtained enhanced image as a target optimized image corresponding to the first image.
And performing size adjustment on the deconvolution image according to the image size of the first image, and taking the obtained enhanced image as a target optimized image corresponding to the first image. After the deconvolution image is obtained, the deconvolution image can be subjected to size adjustment according to the image size of the first image, the enhanced image obtained after the size adjustment is used as a corresponding target optimized image, and the image size of the obtained target optimized image can be the same as the image size of the first image, namely, the image size of the target optimized image is N multiplied by M.
In one embodiment, as shown in FIG. 7, step S170 includes sub-steps S171 and S172.
S171, corresponding size proportion information is obtained according to the image size of the first image and the image size of the deconvolution image.
Specifically, the size ratio information can be obtained by corresponding calculation according to the image size of the first image and the image size of the deconvolution image, and the size ratio information includes the width ratio of the first image to the deconvolution image and the length ratio of the first image to the deconvolution image.
And S172, carrying out up-sampling processing on pixels contained in the deconvolution image according to the size proportion information so as to carry out size adjustment on the deconvolution image to obtain the target optimized image.
The pixels included in the deconvolution image can be up-sampled according to the size proportion information, the up-sampling process is opposite to the down-sampling process, that is, the image can be up-sampled to be amplified and adjusted, that is, the multiple of the amplification adjustment corresponds to the corresponding ratio in the size proportion information, and then the enhanced image obtained after the size adjustment of the deconvolution image can be used as the target optimized image corresponding to the first image.
The technical method can be applied to application scenes including intelligent enhancement processing on image quality, such as intelligent government affairs, intelligent urban management, intelligent community, intelligent security, intelligent logistics, intelligent medical treatment, intelligent education, intelligent environmental protection, intelligent traffic and the like, so that construction of intelligent cities is promoted.
In the image enhancement processing method provided by the embodiment of the invention, a first image input by a user is processed according to a channeling processing rule to obtain a multi-channel characteristic image, channel image information of each channel is extracted from the multi-channel characteristic image, the channel characteristic images of the corresponding channels are respectively whitened according to the channel image information to obtain whitened characteristic images, cross-channel extraction is carried out to obtain cross-channel image information, the whitened characteristic images of each channel are respectively optimized pixel by pixel to obtain corresponding optimized characteristic images, then deconvolution processing is carried out to obtain deconvolution images, and size adjustment is carried out on the deconvolution images to obtain target optimized images. According to the method, whitening processing is respectively carried out on each channel characteristic image according to the channel image information obtained from the first image information, then pixel-by-pixel optimization processing is carried out on the whitened characteristic images of each channel through the cross-channel image information, and each pixel in the image can be respectively enhanced in a targeted manner, so that the effect of enhancing the image is greatly enhanced, intelligent enhancement of the image quality is realized, and the image quality is improved.
The embodiment of the invention also provides an image enhancement processing device which can be configured in the user terminal or the management server and is used for executing any embodiment of the image enhancement processing method. Specifically, referring to fig. 8, fig. 8 is a schematic block diagram of an image enhancement processing apparatus according to an embodiment of the present invention.
As shown in fig. 8, the image enhancement processing apparatus 100 includes a multi-channel feature image acquisition unit 110, a channel image information acquisition unit 120, a whitening feature image acquisition unit 130, a cross-channel extraction unit 140, an optimized feature image acquisition unit 150, a deconvolution image acquisition unit 160, and a target optimized image acquisition unit 170.
The multi-channel feature image obtaining unit 110 is configured to, if a first image input by a user is received, process the first image according to a preset channelization rule to obtain a corresponding multi-channel feature image.
In a specific embodiment, the pixel segmentation processing unit 110 includes a subunit: the second image acquisition unit is used for carrying out size adjustment on the first image according to the size information so as to obtain a second image matched with the size information; and the convolution processing unit is used for carrying out convolution processing on the second image according to the multi-channel convolution check to obtain a corresponding multi-channel characteristic image.
And a channel image information obtaining unit 120, configured to extract channel image information corresponding to the channel feature image of each channel from the multi-channel feature image according to a preset image information extraction rule.
In a specific embodiment, the channel image information obtaining unit 120 includes a subunit: the average value vector obtaining unit is used for calculating the average value vector of each channel characteristic image according to the average value calculation formula; and the covariance matrix acquisition unit is used for calculating the covariance matrix of each channel characteristic image according to the matrix calculation formula and the mean vector.
And a whitening feature image obtaining unit 130, configured to perform whitening processing on the channel feature images corresponding to the channel image information according to the channel image information of each channel, so as to obtain whitening feature images corresponding to each channel.
In a specific embodiment, the whitening feature image acquiring unit 130 includes a subunit: the difference information calculation unit is used for calculating pixel difference values between each pixel value in each channel characteristic image and the mean vector of the channel characteristic image respectively to obtain difference information corresponding to each channel characteristic image; and the whitening characteristic image acquisition unit is used for carrying out inverse transformation on each covariance matrix and multiplying each pixel difference value in the difference value information of the corresponding channel characteristic image respectively to obtain a whitening characteristic image corresponding to each channel characteristic image.
And the cross-channel extraction unit 140 is configured to perform cross-channel extraction on the whitened feature image to obtain corresponding cross-channel image information.
In a specific embodiment, the cross-channel extraction unit 140 includes a subunit: the cross-channel mean value calculation unit is used for carrying out cross-channel mean calculation on a plurality of pixel values corresponding to each pixel in the whitening characteristic image to obtain a cross-channel mean value corresponding to each pixel; the cross-channel standard deviation calculation unit is used for calculating the cross-channel standard deviation of each pixel according to a preset standard deviation calculation formula and the cross-channel mean value.
And the optimized feature image obtaining unit 150 is configured to perform pixel-by-pixel optimization on the whitened feature image of each channel according to a preset optimized model and the cross-channel image information, so as to obtain an optimized feature image corresponding to each channel.
In a specific embodiment, the optimized feature image acquisition unit 150 includes a subunit: the standard pixel value obtaining unit is used for respectively carrying out standardized calculation on each pixel value of each whitening characteristic image according to the standardized calculation formula and the cross-channel image information to obtain a standard pixel value of each pixel in each whitening characteristic image; the convolution dimension reduction calculation unit is used for carrying out convolution dimension reduction calculation on the cross-channel image information according to the convolution operator to obtain dimension reduced cross-channel image information; and the superposition calculation unit is used for carrying out superposition calculation on the standard pixel value of each pixel in each whitening characteristic image and the cross-channel image information after the dimension reduction to obtain an optimized characteristic image corresponding to each whitening characteristic image.
And the deconvolution image acquisition unit 160 is configured to deconvolve the optimized feature image according to a preset deconvolution processing rule to obtain a corresponding deconvolution image.
The target optimized image obtaining unit 170 is configured to resize the deconvoluted image according to an image size of the first image, and take the obtained enhanced image as a target optimized image corresponding to the first image.
In a specific embodiment, the target optimized image acquisition unit 170 includes a subunit: a size proportion information obtaining unit, configured to obtain corresponding size proportion information according to an image size of the first image and an image size of the deconvolution image; and the size adjustment unit is used for carrying out up-sampling processing on pixels contained in the deconvolution image according to the size proportion information so as to carry out size adjustment on the deconvolution image to obtain the target optimized image.
The image enhancement processing device provided by the embodiment of the invention applies the image enhancement processing method, processes the first image input by the user according to the channeling processing rule to obtain a multi-channel characteristic image, extracts channel image information of each channel from the multi-channel characteristic image, respectively whitens the channel characteristic image of the corresponding channel according to the channel image information to obtain a whitened characteristic image, and performs cross-channel extraction to obtain cross-channel image information, respectively optimizes the whitened characteristic image of each channel pixel by pixel to obtain a corresponding optimized characteristic image, then deconvolutes the optimized characteristic image to obtain a deconvoluted image, and adjusts the size of the deconvoluted image to obtain a target optimized image. According to the method, whitening processing is respectively carried out on each channel characteristic image according to the channel image information obtained from the first image information, then pixel-by-pixel optimization processing is carried out on the whitened characteristic images of each channel through the cross-channel image information, and each pixel in the image can be respectively enhanced in a targeted manner, so that the effect of enhancing the image is greatly enhanced, intelligent enhancement of the image quality is realized, and the image quality is improved.
The image enhancement processing means described above may be implemented in the form of a computer program which is executable on a computer device as shown in fig. 9.
Referring to fig. 9, fig. 9 is a schematic block diagram of a computer device according to an embodiment of the present invention. The computer device may be a user terminal or a management server for performing an image enhancement processing method to perform intelligent enhancement processing on image quality.
With reference to FIG. 9, the computer device 500 includes a processor 502, a memory, and a network interface 505, which are connected by a system bus 501, wherein the memory may include a storage medium 503 and an internal memory 504.
The storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032, when executed, may cause the processor 502 to perform an image enhancement processing method, wherein the storage medium 503 may be a volatile storage medium or a nonvolatile storage medium.
The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.
The internal memory 504 provides an environment for the execution of a computer program 5032 in the storage medium 503, which computer program 5032, when executed by the processor 502, causes the processor 502 to perform the image enhancement processing method.
The network interface 505 is used for network communication, such as providing for transmission of data information, etc. It will be appreciated by those skilled in the art that the architecture shown in fig. 9 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting of the computer device 500 to which the present inventive arrangements may be implemented, as a particular computer device 500 may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
The processor 502 is configured to execute a computer program 5032 stored in a memory, so as to implement the corresponding functions in the image enhancement processing method.
Those skilled in the art will appreciate that the embodiment of the computer device shown in fig. 9 is not limiting of the specific construction of the computer device, and in other embodiments, the computer device may include more or less components than those shown, or certain components may be combined, or a different arrangement of components. For example, in some embodiments, the computer device may include only a memory and a processor, and in such embodiments, the structure and function of the memory and the processor are consistent with the embodiment shown in fig. 9, and will not be described again.
It should be appreciated that in an embodiment of the invention, the processor 502 may be a central processing unit (Central Processing Unit, CPU), the processor 502 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSPs), application specific integrated circuits (Application Specific Integrated Circuit, ASICs), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In another embodiment of the invention, a computer-readable storage medium is provided. The computer readable storage medium may be a volatile or nonvolatile computer readable storage medium. The computer readable storage medium stores a computer program which, when executed by a processor, implements the steps included in the image enhancement processing method described above.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus, device and unit described above may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein. Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the units is merely a logical function division, there may be another division manner in actual implementation, or units having the same function may be integrated into one unit, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices, or elements, or may be an electrical, mechanical, or other form of connection.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment of the present invention.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention is essentially or part of what contributes to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a computer-readable storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned computer-readable storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (8)
1. An image enhancement processing method, the method comprising:
if a first image input by a user is received, processing the first image according to a preset channelized processing rule to obtain a corresponding multi-channel characteristic image;
extracting channel image information corresponding to the channel characteristic image of each channel from the multi-channel characteristic image according to a preset image information extraction rule;
respectively performing whitening treatment on channel characteristic images corresponding to the channel image information according to the channel image information of each channel to obtain whitened characteristic images corresponding to each channel;
performing cross-channel extraction on the whitened characteristic image to obtain corresponding cross-channel image information;
respectively carrying out pixel-by-pixel optimization on the whitened characteristic images of each channel according to a preset optimization model and the cross-channel image information to obtain optimized characteristic images corresponding to each channel;
Deconvolution processing is carried out on the optimized feature image according to a preset deconvolution processing rule to obtain a corresponding deconvolution image;
performing size adjustment on the deconvolution image according to the image size of the first image, and taking the obtained enhanced image as a target optimized image corresponding to the first image;
the cross-channel image information comprises a cross-channel mean value and a cross-channel standard deviation, and the cross-channel extraction of the whitened feature image to obtain corresponding cross-channel image information comprises the following steps:
performing cross-channel average calculation on a plurality of pixel values corresponding to each pixel in the whitening characteristic image to obtain a cross-channel average value corresponding to each pixel;
calculating according to a preset standard deviation calculation formula and the cross-channel mean value to obtain the cross-channel standard deviation of each pixel;
the optimization model comprises a standardized calculation formula and a convolution operator, the whitening characteristic image of each channel is respectively subjected to pixel-by-pixel optimization processing according to a preset optimization model and the cross-channel image information to obtain an optimization characteristic image corresponding to each channel, and the optimization model comprises the following steps:
respectively carrying out standardized calculation on each pixel value of each whitening characteristic image according to the standardized calculation formula and the cross-channel image information to obtain a standard pixel value of each pixel in each whitening characteristic image;
Performing convolution dimension reduction calculation on the cross-channel image information according to the convolution operator to obtain dimension reduced cross-channel image information;
and carrying out superposition calculation on the standard pixel value of each pixel in each whitening characteristic image and the cross-channel image information after dimension reduction to obtain an optimized characteristic image corresponding to each whitening characteristic image.
2. The method according to claim 1, wherein the channeling rule includes size information and a multichannel convolution kernel, and the processing the first image according to the preset channeling rule to obtain the corresponding multichannel feature image includes:
performing size adjustment on the first image according to the size information to obtain a second image matched with the size information;
and carrying out convolution processing on the second image according to the multichannel convolution check to obtain a corresponding multichannel characteristic image.
3. The image enhancement processing method according to claim 1, wherein the image information extraction rule includes a mean calculation formula and a matrix calculation formula, the channel image information includes a mean vector and a covariance matrix, the extracting channel image information corresponding to the channel feature image of each channel from the multi-channel feature image according to a preset image information extraction rule includes:
Calculating the mean vector of each channel characteristic image according to the mean calculation formula;
and calculating a covariance matrix of each channel characteristic image according to the matrix calculation formula and the mean value vector.
4. The image enhancement processing method according to claim 3, wherein the performing whitening processing on the channel feature image corresponding to the channel image information according to the channel image information of each of the channels, respectively, to obtain the whitened feature image corresponding to each of the channels, comprises:
calculating pixel difference values between each pixel value in each channel characteristic image and the mean vector of the channel characteristic image respectively to obtain difference information corresponding to each channel characteristic image;
and carrying out inverse transformation on each covariance matrix and multiplying each pixel difference value in the difference value information of the corresponding channel characteristic image respectively to obtain a whitening characteristic image corresponding to each channel characteristic image.
5. The image enhancement processing method according to claim 1, wherein the resizing the deconvoluted image according to the image size of the first image, taking the obtained enhanced image as a target optimized image corresponding to the first image, comprises:
Acquiring corresponding size proportion information according to the image size of the first image and the image size of the deconvolution image;
and carrying out up-sampling processing on pixels contained in the deconvolution image according to the size proportion information so as to carry out size adjustment on the deconvolution image to obtain the target optimized image.
6. An image enhancement processing apparatus for performing the image enhancement processing method according to any one of claims 1 to 5, characterized in that the apparatus comprises:
the multi-channel characteristic image acquisition unit is used for processing the first image according to a preset channeling processing rule to obtain a corresponding multi-channel characteristic image if the first image input by a user is received;
a channel image information acquisition unit, configured to extract channel image information corresponding to a channel feature image of each channel from the multi-channel feature image according to a preset image information extraction rule;
the device comprises a whitening characteristic image acquisition unit, a processing unit and a processing unit, wherein the whitening characteristic image acquisition unit is used for respectively carrying out whitening processing on a channel characteristic image corresponding to the channel image information according to the channel image information of each channel to obtain a whitening characteristic image corresponding to each channel;
The cross-channel extraction unit is used for carrying out cross-channel extraction on the whitening characteristic image to obtain corresponding cross-channel image information;
the optimized characteristic image acquisition unit is used for respectively carrying out pixel-by-pixel optimization processing on the whitened characteristic image of each channel according to a preset optimized model and the cross-channel image information to obtain an optimized characteristic image corresponding to each channel;
the deconvolution image acquisition unit is used for carrying out deconvolution processing on the optimized characteristic image according to a preset deconvolution processing rule to obtain a corresponding deconvolution image;
and the target optimized image acquisition unit is used for carrying out size adjustment on the deconvolution image according to the image size of the first image, and taking the obtained enhanced image as a target optimized image corresponding to the first image.
7. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the image enhancement method according to any one of claims 1 to 5 when executing the computer program.
8. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements the image enhancement processing method according to any one of claims 1 to 5.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9230193B1 (en) * | 2015-05-11 | 2016-01-05 | StradVision, Inc. | Method for increasing object detection rate or object recognition rate and classifier using locally decorrelated channel feature (LDCF) |
| CN109816666A (en) * | 2019-01-04 | 2019-05-28 | 三峡大学 | Symmetrical full convolutional neural networks model building method, eye fundus image blood vessel segmentation method, apparatus, computer equipment and storage medium |
| CN111833360A (en) * | 2020-07-14 | 2020-10-27 | 腾讯科技（深圳）有限公司 | Image processing method, device, equipment and computer readable storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9230193B1 (en) * | 2015-05-11 | 2016-01-05 | StradVision, Inc. | Method for increasing object detection rate or object recognition rate and classifier using locally decorrelated channel feature (LDCF) |
| CN109816666A (en) * | 2019-01-04 | 2019-05-28 | 三峡大学 | Symmetrical full convolutional neural networks model building method, eye fundus image blood vessel segmentation method, apparatus, computer equipment and storage medium |
| CN111833360A (en) * | 2020-07-14 | 2020-10-27 | 腾讯科技（深圳）有限公司 | Image processing method, device, equipment and computer readable storage medium |
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