Single image deraining based on skip fusion connections
SHEN Yan
ZHANG Zhifan
CHEN Zhenghang
LIAO Chen
LI Dan
Abstract：Under complex rainy conditions,U-shaped encoder-decoder networks struggle to remove similar rain streaks appearing at multiple scales and often suffer from the loss of fine-grained details.To address these issues,this paper proposes a Skip Fusion Connection Network(SFCNet)designed to enhance multi-scale feature transmission between the encoder and decoder.First,Skip Fusion Con-nection Block(SFCB)is designed with multi-scale inputs and outputs.By cascading Cross-scale Fea-tures Fusion Blocks(CFFB),this module globally fuses output features across different encoder lev-els.Second,a gating mechanism is integrated to dynamically optimize the attention weights of multi-scale features as they are transmitted to the decoder.This enables each decoder layer to learn rain-context information that matches its specific resolution,significantly improving the removal of dense,overlapping,and multi-scale rain streaks.Finally,to better restore structural details heavily occluded by complex rain patterns,a Detail Enhancement Block(DEB)is proposed to enhance and fuse original image details at different scales back into the network.Experimental results on standard datasets such as Rain200L and DID-Data demonstrate that SFCNet achieves an average PSNR improvement of 0.24 dB over the IDT method.These findings provide an effective approach for single-image deraining under complex rain conditions.
Keywords：single-image derainingSkip Fusion Connection Network(SFCNet)multi-scaledetail enhancement
Publication Date：2025-12-30
Online Publishing Date：2026-02-02(First online date of this platform, not the publication date of the document)
Pages：9( 165-173 )