Visual Quality Enhancement Of Images Under Adverse Weather Conditions

Abstract

The visual quality of an image captured by vision systems can degrade significantly under adverse weather conditions. In this paper we propose a deep learning based solution to improve the visual quality of images captured under rainy and foggy circumstances, which are among the prominent and common weather conditions that attribute to bad image quality. Our convolutional neural network(CNN), NVDeHazenet learns to predict both the original signal as well as the atmospheric light to finally restore image quality. It outperforms the existing state of the art methods by evaluation on both synthetic data as well as real world hazy images. The deraining CNN, NVDeRainNet shows similar performance on existing rain datasets as the state of the art. On natural rain images NVDeRainNet shows better than state of the art performance. We show the use of perceptual loss to improve the visual quality of results. These networks require considerable amount of data under adverse weather conditions and their respective ground truth for training. For this purpose we use a weather simulation framework to simulate synthetic rainy and foggy environments. This data is augmented with existing rain datasets to train the networks.