Recurrent wavelet structure-preserving residual network for single image deraining

The combination of deep learning and image prior has been widely used in single image deraining since 2017. Recent studies have demonstrated an excellent deraining effect on the high-frequency part of rain images, but less attention was paid to the low-frequency part of rain images. The rain streaks remain in the low-frequency part of rain images, thus limiting the deraining effect. Since the rain streaks in rain images are often mixed with object edges and background scenes, it is challenging to separate rain from them by directly learning the deraining function in the image domain. To solve these problems, we propose a novel Recurrent Wavelet Structure-preserving Residual Network (RWSRNet), which mainly preserves and introduces the low-frequency sub-images of each level into the low-frequency rain removal sub-networks that are greatly different from the state-of-the-art approaches introducing wavelet transform. In addition, we also share the low-frequency structure information to the high-frequency sub-networks through block connection, which further enriches the detailed information, facilitates convergence, and strengthens the ability of our network to remove rain streaks in high frequency. Finally, we fuse the derained low-frequency sub-images of each level through the proposed image weighted blending module and finally reconstruct the low- and high-frequency sub-images into clean images through inverse wavelet transform recursively. The experimental results indicate that the proposed method achieves an excellent deraining effect on both low- and high-frequency parts of rain images and has better performance in low-frequency preservation and high-frequency enhancement in comparison with the state-of-the-art approaches on synthetic and real image datasets.