  Image deraining have have gained a great deal of attention in order to
address the challenges posed by the effects of harsh weather conditions on
visual tasks. While convolutional neural networks (CNNs) are popular, their
limitations in capturing global information may result in ineffective rain
removal. Transformer-based methods with self-attention mechanisms have
improved, but they tend to distort high-frequency details that are crucial for
image fidelity. To solve this problem, we propose the Gabor-guided tranformer
(Gabformer) for single image deraining. The focus on local texture features is
enhanced by incorporating the information processed by the Gabor filter into
the query vector, which also improves the robustness of the model to noise due
to the properties of the filter. Extensive experiments on the benchmarks
demonstrate that our method outperforms state-of-the-art approaches.
