Joint feedback and recurrent deraining network with ensemble learning

Yu Luo, Menghua Wu, Qingdong Huang, Jian Zhu, Jie Ling, Bin Sheng

Abstract: Rainy images typically contain heterogeneous rain distributions; however, many existing methods perform well in simple homogeneous rain and fail to handle complex heterogeneous rain effectively. In this paper, we try to solve this problem by fully exploiting the complementary contextual information in the manner of a Joint Feedback and Recurrent deraining scheme with Ensemble Learning (JFREL). First, the proposed JFREL is built on a recurrent multistage architecture, and the output of each stage is fused automatically via ensemble learning. Second, the feedback mechanism is utilized to refine information from inter- and intra-stages. Third, at each stage the residual dilated aggregation attention module is recursively adopted to adequately characterize complementary high-level contextual information in multiple receptive fields and adaptively aggregate beneficial details to achieve feature compensation. Extensive experiments demonstrate that the proposed JFREL can achieve a competitive performance over the state-of-the-art methods on both synthetic and real-world datasets.