Image Restoration on Residual Aggregation Network in Poor Weather Condition

Abstract

Image restoration in poor weather conditions can assist military combatants to efficiently and accurately perform object detection, object recognition and object tracking. Moreover, in security systems, traffic navigation, etc. it also has high application value. Aiming at the problem of image distortion caused by different poor weather conditions like dust, rain, snow, fog, haze, etc. this paper proposes a new deep neural network based image restoration technology, a residual aggregation module is constructed for extracting the detailed features. Furthermore, dense connection is applied to combine low-dimensional features and generate high-dimensional features. The experimental results show that the network achieves superior results in image de-raining(IDR) compared with Deep Detail Network(DDN) and Dual Convolutional Neural Network(DualCNN) while obtaining favorable performances in image de-noising, image de-hazing, image de-blurring, image de-raindrops and other tasks.