基于渐进递归的生成对抗单幅图像去雨算法

doi:10.13229/j.cnki.jdxbgxb.20230756

Abstract

Abstract:

To address the issue of traditional GAN networks underperforming in single-image rain removal due to imbalanced network capacity， this article introduces a progressive recursive generative adversarial algorithm for this task. This method employs a progressive recursive module generator and a multi-scale feature module discriminator， aiming to enhance the efficiency of the generator and bolster the discriminator's capability. The progressive recursive module， by merging multi-scale features and constructing a progressive recursive structure， not only reduces the burden of network parameters but also elevates the generator's efficiency. Concurrently， the multi-scale feature module aids the discriminator in extracting features at both local and global levels， thereby amplifying its discriminative power. Experimental results indicate that， compared to existing algorithms， our method achieves a peak signal-to-noise ratio （PSNR） and a structural similarity index measure （SSIM） were improved by 1.11% and 1.16% on the Rain100L dataset. On the Rain100H dataset， these metrics were improved by 3.28% and 1.01%， respectively. On real-world datasets， our algorithm excels in rain removal， successfully preserving the majority of detailed features. These experimental outcomes thoroughly verify the effectiveness and robustness of our proposed algorithm.

Key words: computer version, image rain removal, multi-scale, generative adversarial networks, progressive recursive

CLC Number:

TP391

Guang-wen LIU,Qi-ying ZHAO,Chao WANG,Lian-yu Gao,Hua CAI,Qiang FU. Progressive recursive generative adversarial network-based single-image rain removal algorithm[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(4): 1363-1373.

Figures/Tables 12

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Table 1

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Table 2

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Table 5

Comparison of loss function parameter settings"

参数	PSNR	SSIM
$α = 110$ ， $β = 1$	35.19	0.935
$α = 100$ ， $β = 10$	35.50	0.962
$α = 60$ ， $β = 50$	34.92	0.949
$α = 10$ ， $β = 100$	34.68	0.947

Table 5

Table 6

References 24

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数据集	Rain100L		Rain100H		Rain400
数据集	PSNR	SSIM	PSNR	SSIM	PSNR	SSIM
DID-MDN^［17］	26.84	0.860	25.00	0.754	25.01	0.728
DDN^［15］	32.38	0.926	24.95	0.781	25.05	0.721
DerainCycleGAN^［13］	31.49	0.936	26.82	0.848	29.24	0.852
RESCAN^［18］	34.99	0.931	26.45	0.846	27.14	0.841
JORDER^［16］	35.11	0.951	22.15	0.674	27.78	0.867
MSPFN^［20］	34.40	0.943	27.62	0.860	29.18	0.896
ID-CGAN^［11］	32.44	0.950	24.16	0.747	29.87	0.898
PRENET^［19］	34.79	0.945	28.06	0.888	29.92	0.902
JRGR^［23］	32.80	0.938	27.40	0.867	31.88	0.938
MOEDN^［24］	34.39	0.945	28.00	0.876	30.12	0.919
本文	35.50	0.962	28.98	0.897	32.32	0.946

评估指标	N=2	N=3	N=4	N=5
PSNR	35.02	35.28	35.50	35.11
SSIM	0.941	0.958	0.962	0.961

模块	PSNR	SSIM
渐进递归模块	33.19	0.902
多尺度特征模块	34.74	0.918
渐进递归模块+多尺度特征模块	35.50	0.962

Progressive recursive generative adversarial network-based single-image rain removal algorithm

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