Abstract:
Quality of outdoor images are often degraded under bad weather conditions by the extensive presence of suspended particles in the atmosphere such as fog and haze. Although, many single image defogging approaches have been proposed in the past, their restoration performance is considerably low since they fail to consider the image-specific cues. To feed that information, we propose a simple and robust defogging framework that can estimate the rough depth map of a foggy image based on the density of the fog in local regions. Obtained depth map information is used to compute the scene-specific dark channel and transmission. In addition, a histogram normalization based post-processing technique is used to enhance the restoration. Experimental evaluation performed on a benchmark dataset demonstrates the proposed defogging framework outperforms state-of-the-art approaches.
