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.

翻译：图像去雨是为了解决恶劣天气条件对视觉任务带来的挑战而受到广泛关注的研究方向。尽管卷积神经网络（CNN）应用广泛，但其在捕捉全局信息方面的局限性可能导致雨纹去除效果不佳。基于自注意力机制的Transformer方法虽有所改进，但容易扭曲对图像保真度至关重要的高频细节。为解决此问题，我们提出了Gabor引导的Transformer（Gabformer）用于单幅图像去雨。通过将Gabor滤波器处理后的信息融入查询向量，增强了对局部纹理特征的关注，同时利用滤波器的特性提高了模型对噪声的鲁棒性。在基准数据集上的大量实验表明，我们的方法优于现有最优技术。