A MULTI-SCALE RECURRENT RESIDUAL ATTENTION FOR SINGLE IMAGE DERAINING

At present, the rain removal methods based on convolution neural network still suffer from residual rain streaks and details lost. This paper proposes a novel network for single image deraining including the multi-scale feature extraction module and the recurrent residual attention. The multi-scale feature map was obtained by constructing the multi-scale Laplacian pyramid. The recurrent residual attention module was designed to promote the connection between stages, extract depth features and enhance the weight of important features, so as to better remove rain streak and preserve the image details. Experimental results demonstrate that the proposed method performs favorably against other state-of-the-art methods.