CN112365559B - Infrared image coloring method for generating countermeasure network based on structural similarity - Google Patents

Infrared image coloring method for generating countermeasure network based on structural similarity Download PDF

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CN112365559B
CN112365559B CN202011305117.2A CN202011305117A CN112365559B CN 112365559 B CN112365559 B CN 112365559B CN 202011305117 A CN202011305117 A CN 202011305117A CN 112365559 B CN112365559 B CN 112365559B
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CN112365559A (en
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朱建清
吴含笑
曾焕强
陈婧
蔡灿辉
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Huaqiao University
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/001—Texturing; Colouring; Generation of texture or colour
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
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    • G06T2207/10048—Infrared image

Abstract

The invention relates to an infrared image coloring method for generating a countermeasure network based on structural similarity, which constructs a countermeasure network based on structural similarity and comprises a generator and a discriminator; the discriminator can distinguish whether the image is from an infrared image domain or a color image domain, and a loss function based on a generation countermeasure network, a cycle consistency loss function and a reconstruction loss function based on a norm or a norm, and a loss function based on structural similarity are adopted for the generator, so that the generator can generate a color image with vivid colors and clear edges; the pre-collected infrared image and the color image are used for training the proposed generation countermeasure network based on the structural similarity to a convergence condition, and the obtained generator can realize coloring of the infrared image. The invention not only can retain the advantage of infrared imaging shooting at night, but also can be beneficial to human eyes to better and more quickly capture useful information in the image and give full play to the value of the image, thereby promoting the development of night vision imaging technology.

Description

Infrared image coloring method for generating countermeasure network based on structural similarity
Technical Field
The invention relates to the technical field of image processing, in particular to an infrared image coloring method for generating a countermeasure network based on structural similarity.
Background
The near-infrared imaging technology is a technology for imaging by reflecting near-infrared radiation by different objects, is widely applied to night vision imaging devices, and has great application in military affairs, field observation, security, navigation and other aspects.
However, since the infrared image obtained by the near-infrared imaging technology is a monochromatic image, compared with a color image, the infrared image lacks more color features, which is not beneficial to direct resolution and identification of human eyes, and restricts the acquisition of effective information in a scene.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an infrared image coloring method for generating a countermeasure network based on structural similarity, which can convert an infrared image into a color image, not only retains the advantage of shooting at night by infrared imaging, but also is beneficial to human eyes to better and more quickly capture useful information in the image.
The technical scheme of the invention is as follows:
an infrared image coloring method for generating a countermeasure network based on structural similarity comprises the following steps:
1) constructing a generating countermeasure network based on the structural similarity, wherein the generating countermeasure network based on the structural similarity comprises a generator G and a discriminator D; wherein the generator G comprises a color image generator G RGB Infrared image generator G NIR Color image generator G RGB For generating colour images, an infrared image generator G NIR For generating an infrared image, the discriminator D being arranged to determine whether the image is from an infrared image domain or a colour image domain;
2) for the discriminator D, a loss function for generating a countermeasure network is adopted; for a generator G, a loss function based on a generated countermeasure network, a cyclic consistent loss function based on a first norm or a second norm, a reconstruction loss function are adopted, a loss function based on structural similarity is introduced, and a new loss function for the generator G is constructed; in step 2), the loss function based on the structural similarity is:
Figure GDA0003700138880000021
wherein, the first and the second end of the pipe are connected with each other,
Figure GDA0003700138880000022
Figure GDA0003700138880000023
wherein the content of the first and second substances,
Figure GDA0003700138880000024
Figure GDA0003700138880000025
Figure GDA0003700138880000026
Figure GDA0003700138880000027
Figure GDA0003700138880000028
Figure GDA0003700138880000029
Figure GDA00037001388800000210
y cycle ＝G RGB [G NIR (y)]；
y recon ＝G RGB (y)；
Figure GDA00037001388800000211
x cycle ＝G NIR [G RGB (x)]；
x recon ＝G NIR (x)；
wherein NIR represents the infrared image domain and RGB represents the color image domain; x represents a real infrared image and y represents a color image;
Figure GDA00037001388800000212
representing by an infrared image generator G NIR The generated infrared image is converted into a digital image,
Figure GDA00037001388800000213
representation by a color image generator G RGB Converting the generated color image; g NIR Representing an infrared image generator, G RGB A representative color image generator; x is a radical of a fluorine atom cycle Indicating cyclically consistent infrared image, y cycle Representing a cyclically consistent color image; x is the number of recon Representing the reconstructed infrared image, y recon Representing the reconstructed color image;
Figure GDA00037001388800000214
a desired operation representing a distribution of image samples;
Figure GDA0003700138880000031
representing a structural similarity-based image transformation loss from the infrared image domain to the color image domain;
Figure GDA0003700138880000032
representing a structural similarity-based image transformation loss from a color image domain to an infrared image domain;
Figure GDA0003700138880000033
representing a structural similarity-based cyclic consensus loss from the infrared image domain to the color image domain;
Figure GDA0003700138880000034
representing a structural similarity-based cyclic consensus loss from the color image domain to the infrared image domain;
Figure GDA0003700138880000035
representing a structural similarity-based reconstruction loss from the infrared image domain to the color image domain;
Figure GDA0003700138880000036
representing a structural similarity-based reconstruction loss from a color image domain to an infrared image domain;
the SSIM represents a structural similarity calculation method, which specifically comprises the following steps:
Figure GDA0003700138880000037
wherein m and n represent a pair of images; mu.s m And mu n A mean value representing the image; sigma m And σ n Representing the variance of the image; sigma mn Representing the covariance of the two images; c 1 And C 2 Is a constant;
3) training the generation countermeasure network constructed in the step 1) based on the structural similarity to a convergence condition by utilizing the pre-collected infrared image and color image to obtain an updated color image generator G RGB Using the updated colour image generator G RGB The infrared image is colored.
Preferably, in step 2), the loss function for the generator G is:
L G ＝λ 1 L gan +λ 2 L cycle +λ 3 L recon +λ 4 L ssim ；
wherein L is gan 、L cycle And L recon Respectively representing loss function and cyclic consistent loss function of generation countermeasure networkAnd reconstructing a loss function; lambda [ alpha ] 1 ＞0、λ 2 ＞0、λ 3 > 0 and lambda 4 > 0 denotes a weight parameter for controlling each loss function.
Preferably, in step 3), the convergence condition is:
Figure GDA0003700138880000038
Figure GDA0003700138880000039
wherein NIR represents the infrared image domain and RGB represents the color image domain; g NIR Representing an infrared image generator, G RGB A representative color image generator; d NIR Discriminator indicating that the decision image is from the infrared image domain, D RGB A discriminator for indicating whether the judgment image is from a color image domain; l is D Representing a loss function, L, for the discriminator D G Representing the loss function for generator G.
Preferably, in step 3), the training is countertraining using the generator G and the discriminator D, and the updated color image generator G is obtained RGB 。
The invention has the following beneficial effects:
the invention relates to an infrared image coloring method for generating a confrontation network based on structural similarity, which comprises the following steps of firstly, constructing a generating confrontation network based on the structural similarity, wherein the generating confrontation network comprises a generator and a discriminator; secondly, a generation countermeasure loss function is adopted for the discriminator, so that the discriminator can distinguish whether the image is from an infrared image domain or a color image domain, and the generator not only adopts a loss function based on a generation countermeasure network, a cycle consistent loss function based on a norm or a norm and a reconstruction loss function, but also introduces a loss function based on structural similarity, so that the generator can generate a color image with vivid color and clear edges; and finally, training the proposed generation countermeasure network based on the structural similarity to a convergence condition by utilizing the pre-collected infrared image and the color image, and coloring the infrared image by the obtained generator.
The invention realizes the conversion of the infrared image into a vivid color image, not only can retain the advantage of shooting at night by infrared imaging, but also is beneficial to the human eyes to better and more quickly capture useful information in the image and give full play to the value of the image, thereby promoting the development of the night vision imaging technology.
Drawings
FIG. 1 is a schematic diagram of an infrared image rendering model for generating a countermeasure network based on structural similarity;
fig. 2 is a schematic diagram of a color image generation process.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The invention provides an infrared image coloring method for generating a countermeasure network based on structural similarity in order to convert an infrared image into a vivid color image, which comprises the following steps:
1) constructing a generating confrontation network based on the structural similarity, wherein the generating confrontation network based on the structural similarity comprises a generator G and a discriminator D; wherein the generator G comprises a color image generator G RGB Infrared image generator G NIR Color image generator G RGB For generating colour images, an infrared image generator G NIR For generating an infrared image, the discriminator D being arranged to determine whether the image is from an infrared image domain or a colour image domain;
2) for the discriminator D, a loss function for generating a countermeasure network is adopted; for a generator G, a loss function based on a generated countermeasure network, a cyclic consistent loss function based on a first norm or a second norm, a reconstruction loss function are adopted, a loss function based on structural similarity is introduced, and a new loss function for the generator G is constructed; in step 2), the loss function based on the structural similarity is:
Figure GDA0003700138880000051
wherein the content of the first and second substances,
Figure GDA0003700138880000052
Figure GDA0003700138880000053
wherein the content of the first and second substances,
Figure GDA0003700138880000054
Figure GDA0003700138880000055
Figure GDA0003700138880000056
Figure GDA0003700138880000057
Figure GDA0003700138880000058
Figure GDA0003700138880000059
Figure GDA00037001388800000510
y cycle ＝G RGB [G NIR (y)]；
y recon ＝G RGB (y)；
Figure GDA00037001388800000511
x cycle ＝G NIR [G RGB (x)]；
x recon ＝G NIR (x)；
wherein NIR represents the infrared image domain and RGB represents the color image domain; x represents a real infrared image and y represents a color image;
Figure GDA00037001388800000512
representing by an infrared image generator G NIR The generated infrared image is converted into an infrared image,
Figure GDA00037001388800000513
representation by a color image generator G RGB Converting the generated color image; g NIR Representing an infrared image generator, G RGB A representative color image generator; x is the number of cycle Indicating cyclically consistent infrared image, y cycle Representing a cyclically consistent color image; x is the number of recon Representing the reconstructed infrared image, y recon Representing the reconstructed color image;
Figure GDA0003700138880000061
a desired operation representing a distribution of image samples;
Figure GDA0003700138880000062
representing a structural similarity-based image transformation loss from the infrared image domain to the color image domain;
Figure GDA0003700138880000063
representing a structural similarity-based image transformation loss from a color image domain to an infrared image domain;
Figure GDA0003700138880000064
representing a structural similarity-based cyclic consensus loss from the infrared image domain to the color image domain;
Figure GDA0003700138880000065
representing a structural similarity-based cyclic consensus loss from the color image domain to the infrared image domain;
Figure GDA0003700138880000066
representing a structural similarity-based reconstruction loss from the infrared image domain to the color image domain;
Figure GDA0003700138880000067
representing a structural similarity-based reconstruction loss from a color image domain to an infrared image domain;
the SSIM represents a structural similarity calculation method, which specifically comprises the following steps:
Figure GDA0003700138880000068
wherein m and n represent a pair of images; mu.s m And mu n A mean value representing the image; sigma m And σ n Representing the variance of the image; sigma mn Representing the covariance of the two images; c 1 And C 2 As constants, the present embodiment is set to 0.0001 and 0.0009, respectively;
3) training the generation countermeasure network constructed in the step 1) based on the structural similarity to a convergence condition by utilizing the pre-collected infrared image and color image to obtain an updated color image generator G RGB Using the updated colour image generator G RGB The infrared image is colored.
In this embodiment, the generation countermeasure network described in step 1) is a generation countermeasure network model (No-Independent-Component-for-Encoding GAN, NICE-GAN) without Independent components for an encoder, where two sets of generation countermeasure networks are included, each set of generation countermeasure network includes one discriminator D and one generator G, and in each set of generation countermeasure network, the generator G and the discriminator D share an encoder E in the discriminator D, that is, the encoder E in the discriminator D is used as an encoder of the generator G. As shown in fig. 1, the image conversion process of NICE-GAN is represented as:
Figure GDA0003700138880000069
Figure GDA00037001388800000610
Figure GDA00037001388800000611
Figure GDA0003700138880000075
x recon ＝G NIR [E NIR (x)]；
y recon ＝G RGB [E RGB (y)]；
wherein NIR represents the infrared image domain and RGB represents the color image domain; x represents a real infrared image and y represents a color image;
Figure GDA0003700138880000071
representing by an infrared image generator G NIR The generated infrared image is converted into an infrared image,
Figure GDA0003700138880000072
representation by a color image generator G RGB Converting the generated color image; g NIR Representing an infrared image generator, G RGB A representative color image generator; x is the number of cycle Representing circularly coherent infrared images, y cycle Representing a circularly consistent color image; x is the number of recon Representing the reconstructed infrared image, y recon Representing the reconstructed color image; g NIR And G RGB An infrared image generator and a color image generator respectively; e NIR As a discriminator D NIR An encoder common to the generators; e RGB As a discriminator D RGB Common to the encoder of the generator. In this embodiment, the generator G and the discriminator D (including the encoder E) are constituted by a convolutional neural network, wherein the generator G includes 6 residual block structures and a deconvolution network layer; discriminator D (including encoder E) is a 7-layer convolutional neural network.
In step 2), the loss function for the generator G is:
L G ＝λ 1 L gan +λ 2 L cycle +λ 3 L recon +λ 4 L ssim ；
wherein L is gan 、L cycle And L recon Respectively representing a loss function, a cycle consistent loss function and a reconstruction loss function of the generated countermeasure network; lambda [ alpha ] 1 ＞0、λ 2 ＞0、λ 3 > 0 and lambda 4 > 0 denotes a weight parameter for controlling the respective loss function, in this embodiment, lambda 1 、λ 2 、λ 3 And λ 4 Set to 1, 10 and 0.001, respectively.
In step 3), the convergence condition is as follows:
Figure GDA0003700138880000073
Figure GDA0003700138880000074
wherein NIR represents the infrared image domain and RGB represents the color image domain; g NIR Representing an infrared image generator, G RGB A representative color image generator; d NIR Discriminator indicating that the decision image is from the infrared image domain, D RGB Signature representing the judged image from a colour image fieldA discriminator; l is a radical of an alcohol D Representing a loss function, L, for the discriminator D G Representing the loss function for generator G.
In step 3), the training is to adopt the generator G and the discriminator D to carry out confrontation training so as to obtain the updated color image generator G RGB . Further, using the updated color image generator G RGB The infrared image is colored, so that the infrared image can be converted into a vivid color image; the generation of the color image is shown in fig. 2.
The above examples are provided only for illustrating the present invention and are not intended to limit the present invention. Changes, modifications, etc. to the above-described embodiments are intended to fall within the scope of the claims of the present invention as long as they are in accordance with the technical spirit of the present invention.

Claims (4)

1. An infrared image coloring method for generating a confrontation network based on structural similarity is characterized by comprising the following steps:
1) constructing a generating countermeasure network based on the structural similarity, wherein the generating countermeasure network based on the structural similarity comprises a generator G and a discriminator D; wherein the generator G comprises a color image generator G RGB Infrared image generator G NIR Color image generator G RGB For generating colour images, an infrared image generator G NIR For generating an infrared image, the discriminator D being arranged to determine whether the image is from an infrared image domain or a colour image domain;
2) for the discriminator D, a loss function for generating a countermeasure network is adopted; for a generator G, a loss function based on a generated countermeasure network, a cyclic consistent loss function based on a first norm or a second norm, a reconstruction loss function are adopted, a loss function based on structural similarity is introduced, and a new loss function for the generator G is constructed; in step 2), the loss function based on the structural similarity is:
Figure FDA0003700138870000011
wherein the content of the first and second substances,
Figure FDA0003700138870000012
Figure FDA0003700138870000013
wherein the content of the first and second substances,
Figure FDA0003700138870000014
Figure FDA0003700138870000015
Figure FDA0003700138870000016
Figure FDA0003700138870000017
Figure FDA0003700138870000018
Figure FDA0003700138870000019
Figure FDA00037001388700000110
y cycle ＝G RGB [G NIR (y)]；
y recon ＝G RGB (y)；
Figure FDA0003700138870000021
x cycle ＝G NIR [G RGB (x)]；
x recon ＝G NIR (x)；
wherein NIR represents the infrared image domain and RGB represents the color image domain; x represents a real infrared image and y represents a color image;
Figure FDA0003700138870000022
representing by an infrared image generator G NIR The generated infrared image is converted into an infrared image,
Figure FDA0003700138870000023
representation by a color image generator G RGB Converting the generated color image; g NIR Representing an infrared image generator, G RGB A representative color image generator; x is the number of cycle Indicating cyclically consistent infrared image, y cycle Representing a cyclically consistent color image; x is the number of recon Representing the reconstructed infrared image, y recon Representing the reconstructed color image;
Figure FDA0003700138870000024
a desired operation representing a distribution of image samples;
Figure FDA0003700138870000025
representing a structural similarity-based image transformation loss from the infrared image domain to the color image domain;
Figure FDA0003700138870000026
structure-based representation of conversion from color image domain to infrared image domainImage conversion loss of similarity;
Figure FDA0003700138870000027
representing a structural similarity-based cyclic consensus loss from the infrared image domain to the color image domain;
Figure FDA0003700138870000028
representing a structural similarity-based cyclic consensus loss from the color image domain to the infrared image domain;
Figure FDA0003700138870000029
representing a structural similarity-based reconstruction loss from the infrared image domain to the color image domain;
Figure FDA00037001388700000210
representing a structural similarity-based reconstruction loss from a color image domain to an infrared image domain;
the SSIM represents a structural similarity calculation method, which specifically comprises the following steps:
Figure FDA00037001388700000211
wherein m and n represent a pair of images; mu.s m And mu n A mean value representing the image; sigma m And σ n Representing the variance of the image; sigma mn Representing the covariance of the two images; c 1 And C 2 Is a constant;
3) training the generation countermeasure network constructed in the step 1) based on the structural similarity to a convergence condition by utilizing the pre-collected infrared image and color image to obtain an updated color image generator G RGB Using the updated colour image generator G RGB The infrared image is colored.
2. The infrared image coloring method for generating a countermeasure network based on structural similarity as claimed in claim 1, wherein in step 2), the loss function for the generator G is:
L G ＝λ 1 L gan +λ 2 L cycle +λ 3 L recon +λ 4 L ssim ；
wherein L is gan 、L cycle And L recon Respectively representing a loss function, a cycle consistent loss function and a reconstruction loss function of the generated countermeasure network; lambda [ alpha ] 1 ＞0、λ 2 ＞0、λ 3 > 0 and lambda 4 > 0 denotes a weight parameter for controlling each loss function.
3. The infrared image coloring method for generating a countermeasure network based on structural similarity as claimed in claim 1, wherein in step 3), the convergence condition is:
Figure FDA0003700138870000031
Figure FDA0003700138870000032
wherein NIR represents the infrared image domain and RGB represents the color image domain; g NIR Representing an infrared image generator, G RGB A representative color image generator; d NIR Discriminator indicating that the decision image is from the infrared image domain, D RGB A discriminator for indicating whether the judgment image is from a color image domain; l is D Representing a loss function, L, for the discriminator D G Representing the loss function for generator G.
4. The infrared image coloring method for generating countermeasure network based on structural similarity as claimed in claim 1, which isCharacterized in that in the step 3), the training is countertraining by adopting a generator G and a discriminator D, and then the updated color image generator G is obtained RGB 。
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