Single Image Deraining via Rain-Steaks Aware Deep Convolutional Neural Network

TL;DR

This paper introduces a rain-steaks aware deep CNN that combines image processing and deep learning to effectively remove rain steaks from single rainy images, improving clarity for applications like autonomous navigation.

Contribution

It proposes an improved weighted guided image filter and a rain-steaks aware CNN, enhancing rain steak detection and removal with better efficiency and explainability.

Findings

Significantly outperforms existing methods on synthetic images

Effective on real-world rainy images

Improves image clarity for autonomous navigation

Abstract

It is challenging to remove rain-steaks from a single rainy image because the rain steaks are spatially varying in the rainy image. This problem is studied in this paper by combining conventional image processing techniques and deep learning based techniques. An improved weighted guided image filter (iWGIF) is proposed to extract high frequency information from a rainy image. The high frequency information mainly includes rain steaks and noise, and it can guide the rain steaks aware deep convolutional neural network (RSADCNN) to pay more attention to rain steaks. The efficiency and explain-ability of RSADNN are improved. Experiments show that the proposed algorithm significantly outperforms state-of-the-art methods on both synthetic and real-world images in terms of both qualitative and quantitative measures. It is useful for autonomous navigation in raining conditions.