Single image fog removal by modified DCP through adaptive retinal mechanism.

We propose an image processing model based on Dark Channel Prior (DCP) and the Adaptive Retinal Mechanisms' function to eliminate fog. Target detection systems are examples of computer vision systems surveillance, and pattern recognition is plagued due to these issues in this study; we present a simple and effective strategy for considerably reducing artifacts in recovered photos while using the ordinary dark channel by modifying the artifacts the dark channel computation. We want to use several approaches to solve image problems with heavy fog. The bipolar cells center-surround opponent mechanisms, and input gratitude cells give way to Interplexiform (IP) cells, which eventually give way to horizontally cells, which aid in enhancing the output image's borders and intensities. For color enhancement and correction, the colored function of cell bodies is exploited. Finally, Using a luminance-based fusion methodology, we reproduce the improved picture from either the outputs of an on or off-pathway of the fisheye. According to our findings, our method exceeds several government methods in terms of efficiency and restoration quality. [ABSTRACT FROM AUTHOR]