Multi-feature based Hazy Image Classification for Vision Enhancement

Abstract

Deteriorating contrast, low glow, restricted dynamic range, poor resolution details, non-bright natural landscape colours, and reduced saturation of an image are subjected to various degrees of influence and deterioration in hazy weather conditions. Dehazing haze degraded images becomes challenging if they are not classified as hazy or clear, given that image dehazing techniques can only be used with hazy images. The competence to differentiate between hazy and clear images can not be left to human perception; hence a robust model is needed that classifies the input image into hazy and clear. Thus, we propose a nine unique features-based image classification framework based on K-Nearest Neighbour (KNN), which can accurately classify hazy and clear images. Experimental results demonstrate that the proposed method can efficiently classify the hazy and clear images, with an accuracy of 92%, a precision of 0.90, recall of 0.96, and F1 score of 0.93 for a benchmark dataset, which has both theoretical and practical implications.