Enhanced Deep Learning Approach for Robust Image Dehazing in Varied Atmospheric Conditions

This paper presents a deep learning approach to effective dehazing from images taken in different atmospheric conditions. In these greatly preprocessed images, the color contrast reduces, and the haze only degrades the quality of the picture by adding noise. Contrarily, the so-formed aerial color ambiguity is counted among the issues related to the exact transmission map estimation from achromatic. Overall, this approach surpasses the preceding in the aspect of error rates and the hap consistency of the dehazed images. We propose two novel statistics-based metrics for the assessment of natural outdoor image statistics, including a new and improved framework for the evaluation of proved dehazing algorithms. Overall improvement over the state-of-the-art is made by our method to solve the complexities of image degradation due to haze and also puts up the new benchmark in the performance diagnosis; some of these improvements have been noted to be significant in visibility and color fidelity, with marked falls in the mean square error and higher standard deviation accuracy. Thereby, leading to high potential clarity increase or increases in the number of image details in almost all atmospheric conditions. Therefore, it is still hopeful to have an increase in clarity or the number of details in the pictures taken in nearly all atmospheric conditions. Overall, our work attempts to make images useful in many applications, like remote sensing, self driving vehicles, surveillance, and much more, where quality visuals are vital.