融合对比学习和生成对抗网络的图像去雾算法

doi:10.13229/j.cnki.jdxbgxb.20240111

Abstract

Abstract:

Aiming at the limitations of some current defogging algorithms caused by using foggy and non-foggy image pairs and the cost consumption caused by supervised learning， this paper proposes an image defogging algorithm based on comparative learning and recurrent consistent generative adversarial network. By training recurrent generative adversarial network with unpaired foggy and clear images， the value of image defogging algorithm in real scenes is improved， and the domain shift problem of defogging algorithm is alleviated； meanwhile， we design the contrast-guided branch to learn the potential feature distribution of the image， implicitly constrain the embedding of different samples in the depth feature space， deeply mine the similar features of foggy and clear images， pull the similar characteristics of the images closer together， retain the mutual information between the two types of images， maintain the consistency of image content， and improve the performance of network defogging； introduce the frequency loss， constrain the output of the generator， reduce the loss of information in the frequency domain， further retain the content and structural information of the image， reduce the blurring and distortion of the defogged image， and improve the quality and clarity of the generated image. Experimental results show that the model proposed in this paper is an effective image defogging algorithm with improved information entropy and average gradient and richer detail information compared to the current mainstream deep learning-based and traditional defogging algorithms.

Key words: image defogging, unpaired images, generative adversarial networks, contrast learning

CLC Number:

TP391

Xiang-long LUO,Xin-yu WEI,Mao-jun ZHAO,Ruo-chen LIU. Image dehazing algorithm based on contrast learning and generative adversarial network[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(10): 3296-3308.

Figures/Tables 20

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Table 1

Detailed parameters of the generator G"

生成器 $G$	卷积核大小 $k$ 与数量 $n$	网络层输出
编码模块	$k = 7, n = 64$	$256 × 256 × 64$
	$k = 3, n = 128$	$128 × 128 × 128$
	$k = 3, n = 256$	$64 × 64 × 256$
多尺度特征提取模块（ $× 3$ ）	$k = 1, n = 64$	$64 × 64 × 256$
	$k = 1, n = 128; k = 3, n = 64$
	$k = 1, n = 128; k = 5, n = 64$
	最大池化， $k = 1, n = 64$
	$k = 3, n = 256, k = 3, n = 256$	$64 × 64 × 256$
	$k = 3, n = 256, k = 3, n = 256$	$64 × 64 × 256$
解码模块	$k = 3, n = 128$	$128 × 128 × 128$
	$k = 3, n = 64$	$256 × 256 × 64$
	$k = 7, n = 3$	$256 × 256 × 3$

Table 1

Table 2

Generator detailed parameters F"

生成器 $F$	网络层参数	网络层输出
编码模块	$k = 3, n = 64, s = 1$ $k = 3, n = 64, s = 1$ $k = 3, n = 64, s = 2$ $k = 3, n = 128, s = 1$ $k = 3, n = 128, s = 1$ $k = 3, n = 128, s = 2$ $k = 3, n = 256, s = 1$ $k = 3, n = 256, s = 1$ $k = 3, n = 256, s = 2$ $k = 3, n = 512, s = 1$ $k = 3, n = 512, s = 1$ $k = 3, n = 512, s = 2$	$256 × 256 × 64$ $128 × 128 × 64$ $128 × 128 × 128$ $64 × 64 × 128$ $64 × 64 × 256$ $32 × 32 × 256$ $32 × 32 × 512$ $16 × 16 × 512$
解码模块	$k = 3, n = 1024, s = 1$ $k = 3, n = 1024, s = 1$	$16 × 16 × 1024$
	$k = 3, n = 512, s = 2, o p = 1$	$32 × 32 × 512$
	$k = 3, n = 512, s = 1$ $k = 3, n = 512, s = 1$	$32 × 32 × 512$
	$k = 3, n = 256, s = 2, o p = 1$	$64 × 64 × 256$
	$k = 3, n = 256, s = 1$ $k = 3, n = 256, s = 1$	$64 × 64 × 256$
	$k = 3, n = 128, s = 2, o p = 1$	$128 × 128 × 128$
	$k = 3, n = 128, s = 1$ $k = 3, n = 128, s = 1$	$128 × 128 × 128$
	$k = 3, n = 64, s = 2, o p = 1$	$256 × 256 × 64$
	$k = 3, n = 64, s = 1$ $k = 3, n = 64, s = 1$	$256 × 256 × 64$
	$k = 3, n = 3, s = 1$	$256 × 256 × 3$

Table 2

Table 3

Detailed parameters of discriminator"

判别器模块	卷积核大小 $k$ 与数量 $n$	网络层输出
第一层	$k = 3, n = 64$	$128 × 128 × 64$
第二层	$k = 3, n = 128$	$64 × 64 × 128$
第三层	$k = 3, n = 256$	$32 × 32 × 256$
第四层	$k = 3, n = 512$	$32 × 32 × 512$
输出	$k = 3, n = 1$	$32 × 32 × 1$

Table 3

Fig.11

Table 4

Performance comparison of different algorithms on RealData dataset"

去雾方法	IE $↑$	AG $↑$	NIQE $↓$
DCP^［6］	7.061	5.533	7.407
CAP^［7］	7.136	5.154	7.800
FFA-Net^［27］	7.033	6.665	7.573
GCA-Net^［28］	7.216	6.145	7.223
CycleGAN^［21］	7.238	7.677	5.951
Cycle-Dehaze^［15］	7.280	7.397	5.338
C-CycleGAN	7.300	8.039	5.464

Table 4

Fig.12

Table 5

Performance comparison of different algorithms on RTTS dataset"

去雾方法	IE $↑$	AG $↑$	NIQE $↓$
DCP^［6］	6.959	5.183	7.040
CAP^［7］	7.065	4.692	7.651
FFA-Net^［27］	7.007	4.681	7.322
GCA-Net^［28］	7.282	6.162	7.761
CycleGAN^［21］	7.225	7.752	5.632
Cycle-Dehaze^［15］	7.274	7.480	4.956
C-CycleGAN	7.292	8.065	5.150

Table 5

Table 6

Results of ablation experiments on RealData dataset"

实验	IE $↑$	AG $↑$	NIQE $↓$
实验1	7.214	7.458	5.735
实验2	7.269	7.792	5.682
实验3	7.258	7.817	5.896
实验4	7.300	8.039	5.464

Table 6

Fig.13

Table 7

Results of ablation experiments on RTTS dataset"

实验	IE $↑$	AG $↑$	NIQE $↓$
实验1	7.195	7.475	5.532
实验2	7.284	7.895	5.376
实验3	7.268	7.783	5.521
实验4	7.292	8.065	5.150

Table 7

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