Single image deraining aims at generating a clear image from its raining counterpart, and the convolutional neural network based methods have achieved great success. We propose an effective long-range attention based network for single image deraining. Existing attention module relies on current feature as input and generates attention to current feature, so we adopt the information-growth attention to enhance the current feature by using former features. Then a multi scale information-growth self-attention is proposed to extract the long-range representations, and it divides features into multiscales in attention generation process, and its lager receptive field provides more information and more accurate attention with less computation cost. The experiments on several datasets demonstrate that our method achieve better results than the recent state-of-art methods.
Hu et al. (Mon,) studied this question.