MLTDNet: an efficient multi-level transformer network for single image deraining

Feng Gao, Xiangyu Mu, Chao Ouyang, Kai Yang, Shengchang Ji, Jie Guo, Haokun Wei, Nan Wang, Lei Ma, Biao Yang

Abstract: Single image rain streaks removal is a great challenging task in computer vision due to the uncertainty of the shape and size of rain streaks. Current methods attempted to adopt complex optimization processes or progressive refinement schemes. But these methods cause a significant impact on the efficiency of many real-time demanding applications. To address this problem, we propose a multi-level transformer deraining network which is an efficient single image rain removal model. Specifically, an efficient deraining network is constructed to extract rain streaks. We then employ cascade networks to extract feature information from deep high-level to shallow low-level layers. In addition, the multi-head self-attention mechanism is applied to extracting global information in the feature map at each level, which can highly improve the representational ability for rain streaks. Experimental results on both synthetic and real-world datasets have demonstrated the efficacy of our method, which uses less time costs and obtains comparable results in comparison to the state-of-the-art methods.