EAGNet: Elementwise Attentive Gating Network-Based Single Image De-Raining With Rain Simplification

Abstract

Rain streaks are one of the main factors that degrade the performance of computer vision algorithms. Therefore, a preprocessing method is needed to remove rain streaks from rainy images. The main issue of the rain removal task is to prevent over (or under) de-raining. Over de-raining means that the background details are removed along with rain streaks in light rain, and under de-raining means that the rain streaks are not completely removed in heavy rain. These occur as the density of rain and intensity of rain streaks vary. In order to solve this, this paper proposes a two-step rain removal method. The proposed system first estimates the rain streaks image redefined with a simple operation from an input rainy image. The proposed rain streaks image contains rain density and rain streak intensity for the rainy image. By using this, the proposed system can adaptively remove rain streaks from images captured in various rain conditions. In addition, we propose a novel architectural unit, the elementwise attentive gating block, which is an optimized block used to deal with high frequency rain streaks. The proposed block selectively passes the desired components from the input feature maps by applying different weights to each element. It helps to clearly extract the rain streaks, and as a result, there are no traces of rain streaks on the restored image. The proposed method outperforms previous rain removal algorithms for both synthetic and real-world images.