Gradient flow-based deep residual networks for enhancing visibility of scenery images degraded by foggy weather conditions

Abstract

In the recent years, the vehicles are incorporated with camera-based modern driver support systems for facilitating the drivers to confirm their safety under different conditions of driving. However, lower contrast and faded scene visibility is considered as the main issue faced by the driver assistance system while driving in foggy weather conditions. At this juncture, deep neural network methods are considered to be potent in solving the limitations of manually designing haze-related features. In this paper, gradient flow-based deep residual network is utilized for improving the scenery images which are degraded through foggy weather conditions. This proposed scheme uses an undetermined complex function for mathematically modeling the fog in an image, which can be subsequently approximated by the deep residual network into the corresponding mathematical model associated with the fog. This proposed scheme uses two predominant steps that correspond to the determination of transmission map related to the haze image input and removal of foggy haze using residual network based on the estimation of the ratio between transmission map and foggy image. It is considered to be phenomenal in realizing generalization and robustness with minimal input for different unidentified image data. The experimental investigation of the proposed scheme is conducted using NYU2 depth dataset for the purpose of training the utilized residual deep networks. The experimental results proved that the proposed scheme is predominant over the benchmarked fog removal approaches in terms of evaluation metrics such as natural image quality evaluator aspect, blind/reference less image spatial quality evaluator, spatial–spectral entropy-based quality, full-reference metric peak signal to noise ratio, no-reference metric, feature similarity and structural similarity.