Hybrid Approach for Image Defogging Process based on Atmospheric Light Estimation Process

Abstract

Due to unfavorable weather circumstances, images captured from multiple sensors have limited the contrast and visibility. Many applications, such as web camera surveillance in public locations are used to identify object categorization and capture a vehicle's licence plate in order to detect reckless driving. The traditional methods can improve the image quality by incorporating luminance, minimizing distortion, and removing unwanted visual effects from the given images. Dehazing is a vital step in the image defogging process of many real-time applications. This research article focuses on the prediction of transmission maps in the process of image defogging through the combination of dark channel prior (DCP), transmission map with refinement, and atmospheric light estimation process. This framework has succeeded in the prior segmentation process for obtaining a better visualization. This prediction of transmission maps can be improved through the statistical process of obtaining higher accuracy for the proposed model. This improvement can be achieved by incorporating the proposed framework with an atmospheric light estimation algorithm. Finally, the experimental results show that the proposed deep learning model is achieving a superior performance when compared to other traditional algorithms.