  Despite the superiority of convolutional neural networks (CNNs) and
Transformers in single-image rain removal, current multi-scale models still
face significant challenges due to their reliance on single-scale feature
pyramid patterns. In this paper, we propose an effective rain removal method,
the dual-path multi-scale Transformer (DPMformer) for high-quality image
reconstruction by leveraging rich multi-scale information. This method consists
of a backbone path and two branch paths from two different multi-scale
approaches. Specifically, one path adopts the coarse-to-fine strategy,
progressively downsampling the image to 1/2 and 1/4 scales, which helps capture
fine-scale potential rain information fusion. Simultaneously, we employ the
multi-patch stacked model (non-overlapping blocks of size 2 and 4) to enrich
the feature information of the deep network in the other path. To learn a
richer blend of features, the backbone path fully utilizes the multi-scale
information to achieve high-quality rain removal image reconstruction.
Extensive experiments on benchmark datasets demonstrate that our method
achieves promising performance compared to other state-of-the-art methods.
