Performance Comparison.

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.