  Rain-by-snow weather removal is a specialized task in weather-degraded image
restoration aiming to eliminate coexisting rain streaks and snow particles. In
this paper, we propose RSFormer, an efficient and effective Transformer that
addresses this challenge. Initially, we explore the proximity of convolution
networks (ConvNets) and vision Transformers (ViTs) in hierarchical
architectures and experimentally find they perform approximately at intra-stage
feature learning. On this basis, we utilize a Transformer-like convolution
block (TCB) that replaces the computationally expensive self-attention while
preserving attention characteristics for adapting to input content. We also
demonstrate that cross-stage progression is critical for performance
improvement, and propose a global-local self-attention sampling mechanism
(GLASM) that down-/up-samples features while capturing both global and local
dependencies. Finally, we synthesize two novel rain-by-snow datasets,
RSCityScape and RS100K, to evaluate our proposed RSFormer. Extensive
experiments verify that RSFormer achieves the best trade-off between
performance and time-consumption compared to other restoration methods. For
instance, it outperforms Restormer with a 1.53% reduction in the number of
parameters and a 15.6% reduction in inference time. Datasets, source code and
pre-trained models are available at \url{https://github.com/chdwyb/RSFormer}.
