Subband Differentiated Learning Network for Rain Streak Removal

Due to the adverse effects of far more complex rain streaks on the performance of outdoor vision systems, it is critical to accurately eliminate rain streaks from a single rainy image. Recently, many algorithms have made significant progress in rain streak removal. However, these approaches do not give enough consideration to the established fact that rain streaks reflected in different wavelet subbands may have distinct spatial and intensity distributions, resulting in residual degraded components in subbands if not addressed properly. To deal with this problem, we propose a subband differentiated learning network (SDLNet) for rain streak removal. First, we put forward a wavelet subband mask module (WSMM) to produce a wavelet mask, in which different concentrations of rain streak features are provided for different subbands. Second, we advance a WSMM-driven repetitive learning resblock (WSMM-driven RLR) with a novel repetitive learning mechanism (RLM). This RLM enables the WSMM-driven RLR to further probe rain streak features. Third, we propose an adaptive wavelet subband loss (AWSL) function, with the same loss structure but a different loss weight for each subband, exploiting unique error priors in subbands. Finally, the proposed SDLNet employs both a UNet-like architecture with the novel WSMM-driven RLR and the AWSL function, generating a derained image. Compared with state-of-the-art methods, experimental results show that our proposed SDLNet achieves better rain streak elimination performance, where the average PSNR of derained images is improved up to 1.86 dB, while recovering more texture details of clean images with better subjective quality in recovered images.