Visual Quality Enhancement in Challenging Weather using Mutual Entropy Techniques.

In autonomous driving, capturing high-quality images with visual sensors in adverse weather conditions presents a significant challenge for object detection. This paper introduces a candid and effective preprocessing method called Contrast Enhancement through Mutual Entropy (CEME) to improve the visual quality of images. Unlike previous methods such as traditional image processing, image restoration, and deep learning techniques, CEME enhances image quality using simple filtering operations. CEME works by adjusting gray levels appropriately through the calculation of mutual entropy between adjacent gray levels in each plane of a color image. Experimental simulations were conducted on various images taken in weather conditions like snow, fog, sand, and rain. To evaluate performance, this study used two natural image quality assessment metrics: Novel Blind Image Quality Assessment (NBIQA) and Natural Image Quality Evaluator (NIQE). The proposed method achieved an average NBIQA for sandy, snowy, rainy, and foggy images of 28.1576, 35.7233, 29.8796, and 36.1944 respectively. [ABSTRACT FROM AUTHOR]