Lightweight Deep Extraction Networks for Single Image De-raining

Abstract

In bad weather, artifacts such as rain streaks degrade the image quality. In addition, artifacts in the damaged image obstruct human vision and adversely affect the accuracy of object detection. Hence, single image rain removal is an important issue to improve image quality. However, state-of-the-art methods have limitation that require a lot of training data. This paper proposes a lightweight Deep Extraction Network (DEN), which performs well on image de-raining even with a small training dataset. Particularly, we design a novel Light Residual Block (LRB), which is connected in five cascading layers for extracting a deep local feature. Furthermore, DEN deploys a residual learning for training only artifacts. The experimental results on synthetic and real-world rainy image demonstrate the effectiveness in terms of visual and quantitative performance.