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

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 identify object categorization capture a vehicle's licence plate order detect reckless driving. The traditional methods can improve image quality by incorporating luminance, minimizing distortion, removing unwanted visual effects given images. Dehazing is vital step defogging process of many real-time applications. This research article focuses on prediction transmission maps through combination dark channel prior (DCP), map with refinement, atmospheric light estimation process. framework has succeeded segmentation for obtaining better visualization. be improved statistical higher accuracy proposed model. improvement achieved an algorithm. Finally, experimental results show that deep learning model achieving superior performance when compared other algorithms.