Multi-fusion Network for Single Image Deraining

Abstract

Single image deraining is regarded as an important research direction in image processing. To tackle the over-smoothing effect caused by the overlapping between rain streaks and the background, we propose a multi-fusion network for single image deraining. A novel local feature fusion block and a global feature fusion block are explored to fuse the high-level features with the low-level ones and correct the low-level representations. By stacking multiple fusion blocks, the proposed network can fully utilize the high-level information and extract powerful feature maps of rain streak layers. In addition, based on the prediction difficulty, a curriculum learning strategy is further explored to make the training process easier. Extensive experiments demonstrate that our network performs favorably against other deraining approaches.