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In this paper, a novel hybrid network called ChessFormer is proposed for the single image de-rain task. The network seamlessly integrates the advantages of Transformer and fitted neural network (CNN) in a checkerboard architecture, fully utilizing the global modeling capability of Transformer and the local feature extraction efficiency of CNN.ChessFormer adopts a multilevel feature extraction and progressive feature fusion strategy to efficiently achieve the rain line while preserving the We design a multidimensional transposed attention (MSTA), which enhances the network fusion for different rain patterns and mechanism image textures by combining self-attention with gated phase operation. In addition, the efficient architecture ensures full integration of features across dimensions and codecs. Experimental results show that ChessFormer outperforms existing methods in terms of quantitative metrics and visual quality on multiple benchmark datasets, achieving state-of-the-art performance with fewer parameters.

本文针对单图像去雨任务，提出了一种名为ChessFormer的新型混合网络。该网络以棋盘架构无缝融合了Transformer（Transformer）与卷积神经网络（CNN）的优势，充分利用Transformer的全局建模能力与卷积神经网络的局部特征提取效率。ChessFormer采用多级特征提取与渐进式特征融合策略，可在高效去除雨线的同时保留图像细节；我们设计了一种多维转置注意力（MSTA）模块，该模块将自注意力与门控相位操作相结合，能够针对不同雨型与图像结构纹理增强网络的特征融合效果。此外，该高效架构可实现跨维度与编解码模块间的特征充分融合。实验结果表明，ChessFormer在多个基准数据集上的量化指标与视觉质量均优于现有方法，以更少的参数量实现了当前最优性能。