Two-Stage Fusion Model for Heavy Rain Removal on Single Image

Abstract

Current single image derain methods cannot solve the heavy rain situation well. In this paper, based on the physical model of a rainy image, we build a two-stage network, TSF-Net, which combines model-driven and data-driven methods. The first stage gets the rain streaks, atmospheric light, and transmission map to obtain the coarse rain-free image by the physical model. The second stage is a fully convolutional neural network with the structure of U-Net. The proposed Multi-Scale Projection Fusion Block (MSPFB) module, which can perceive the spatial information across the scale of images, is employed to remove the residual rain and fuzzy parts in the first stage and obtain a refined clean image. Extensive experiments show that our TSF-Net achieves better accuracy and visual improvements against state-of-the-art methods.