Color Image Defogging in Dark Channel Prior Based Image Local Features

Abstract

Bad weather, for example fog, haze, smog, etc. Nice to advanced video surveillance will be enhanced, so the needs of pattern recognition AI and related research will increase. The problem is that the contrast of the photographed image decreases, the distant visibility deteriorates greatly, the components, size, and shape of fogs are different but the adverse effects are similar. This study is based on a large amount of analysis of predecessors on the Dark channel prior defogging algorithm based on the research results, especially the He team's research results, depth of field processing, to abandon the He team in order to enhance the sense of depth to set the range of depth of field approach. In this research, we proposed a method to remove fog from uneven defogging image with fog density change, using dark channel prior and local gradient key point extraction technology. Estimate the transmission map by using the image local information and shaping the coarse map using the detailed map. In this way, a clear image is obtained while maintaining the fog removing quality.