New Single Image Rain Removal Algorithm Based on Dual Parallel Branch Residual Overlay Network.

The end-to-end convolutional neural network models have been widely used in single image de-raining, which can extract clean background images from rainy images. However, they suffer from gradient vanishing with increased network depth. With the aim of tackling this problem, this paper proposes an effective rain removal algorithm based on dual parallel branch residual overlay network (DBRONet). Firstly, the two parallel branches with different functions are combined with increasing the width of the network, which can reduce the depth of the network effectively. Secondly, the upper branch uses multi-scale rain streak extraction blocks (MRSEB) composed of multi-scale residual blocks and extracts rain streaks of different densities, sizes and directions in the rainy images. The lower branch utilizes dilated convolution attention residual block (DARB) to expand the receptive field and obtains more context information without increasing the depth of the network. Finally, the de-raining image is obtained by superposition features of the two branches and the original image. Experimental results on synthetic and real datasets show that DBRONet can effectively reduce the depth of the network and the number of parameters. Compared with the existing methods in terms of quantitative and qualitative indicators, it has achieved the most advanced results. When comparing with other methods on Rain100H, Rain100L, Rain12, Rain1400 with the improvements of 0.76 dB, 0.29 dB, 0.11 dB and 0.25 dB on PSNR value and 0.4%, 0.3%, 0.4%, 1.2% on SSIM value, respectively. The source code can be found at https://github.com/RemeberMeX/DBRONet. [ABSTRACT FROM AUTHOR]