基于高阶递归网络的单幅图像去雨滴模型

doi:10.12305/j.issn.1001-506X.2026.01.02

Abstract

Abstract:

At present, the ability of extracting large-scale raindrop features from single image raindrop models is poor, resulting in low accuracy and inability to be well applied in complex and varied practical scenes. Therefore, a single image raindrop removal model based on high-order recursive network is proposed. First, a dual-scale attention residual module by using group convolution combined with attention mechanism is constructed. It better extracts effective features of large-scale raindrops. Second, a high-order recursive feature transfer mechanism is designed to effectively enhance the transfer process of these features from local to global. Last, a dual-scale residual gated recurrent unit is proposed, which establishes feedback process on the features of each stage in the recursive calculation, further improving the model’s performance. The experimental results show that the proposed high-order recursive network has achieved the best performance on publicly available benchmark datasets, effectively solving the problem of insufficient accuracy in current algorithms.

Key words: high-order recursive, deep learning, single image raindrop removal, dual-scale residual, group convolution, gated recurrent unit

CLC Number:

TP 391.4

Yugang BAO, Haoxiang JIA, Danfeng ZHAO. Single image raindrop removal model based on high-order recursive network[J]. Systems Engineering and Electronics, 2026, 48(1): 12-21.

Figures/Tables 11

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References 33

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模块结构	Test_A		Test_B
模块结构	PSNR/dB	SSIM	PSNR/dB	SSIM
标准卷积（3,3）	25.11	0.883	22.89	0.785
标准卷积（3,5）	25.33	0.883	22.93	0.783
单尺度组卷积（1,1）	26.51	0.895	23.46	0.793
单尺度组卷积（3,3）	31.11	0.931	25.89	0.820
单尺度组卷积（5,5）	31.16	0.932	25.90	0.822
双尺度组卷积（1,3）	30.70	0.929	25.76	0.819
双尺度组卷积（1,5）	31.25	0.933	26.03	0.823
双尺度组卷积（3,5）	31.41	0.934	26.17	0.824

超参数组合	Test_A		Test_B
超参数组合	PSNR/dB	SSIM	PSNR/dB	SSIM
R₁=1，R₂=1	25.09	0.815	22.39	0.726
R₁=1，R₂=2	21.37	0.642	19.80	0.594
R₁=1，R₂=3	17.94	0.488	17.42	0.477
R₁=2，R₂=1	30.61	0.928	25.92	0.820
R₁=2，R₂=2	31.41	0.934	26.17	0.824
R₁=2，R₂=3	30.70	0.932	25.75	0.822
R₁=3，R₂=1	30.28	0.924	25.73	0.816
R₁=3，R₂=2	30.96	0.928	26.04	0.820
R₁=3，R₂=3	30.78	0.928	25.93	0.821

模型结构	Test_A		Test_B
模型结构	PSNR/dB	SSIM	PSNR/dB	SSIM
无门控循环单元	30.74	0.927	25.72	0.813
局部递归循环单元	31.09	0.932	25.93	0.822
全局递归循环单元	30.86	0.930	25.79	0.819
双尺度残差循环单元	31.41	0.934	26.17	0.824

模型	Test_A		Test_B
模型	PSNR/dB	SSIM	PSNR/dB	SSIM
ATT-GAN^[7]	24.91	0.884	24.92	0.809
TransWeather^[8]	28.83	0.911	—	—
AG-GAN^[10]	25.09	0.822	22.72	0.738
SA-GAN^[12]	25.56	0.847	22.77	0.753
DuRN-S-P^[13]	28.07	0.887	24.65	0.824
All-in-One^[14]	23.41	0.872	—	—
D-DAM^[15]	30.12	0.926	—	—
Two-stage^[16]	30.12	0.910	24.86	0.803
MANAS^[17]	28.88	0.907	23.66	0.787
DTN^[18]	28.28	0.905	24.96	0.809
QPC-EPG^[20]	29.48	0.913	—	—
本文模型	31.41	0.934	26.17	0.824

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Single image raindrop removal model based on high-order recursive network

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