Enhancement of Hazy Image Using Visibility Restoration Technique

The visibility of outdoor images captured in inclement weather is often degraded due to the presence of haze, fog, sandstorms and so on. Poor visibility caused by atmospheric phenomena in turn causes failure in computer vision applications, such as obstacle detection systems, outdoor object recognition systems, and intelligent transportation systems and video surveillance systems. In order to solve this problem, visibility restoration techniques have been developed and play an important role in many computer vision applications that operate in various weather conditions. However, removing haze from a single image with a complex structure and color distortion is a difficult task for visibility restoration techniques. This paper proposes a novel visibility restoration method that uses a combination of three major modules A depth estimation (DE) module, A color analysis (CA) module, and A visibility restoration (VR) module. The proposed depth estimation module takes advantage of the median filter technique and adopts adaptive gamma correction technique. By doing so, halo effects can be avoided in images with complex structures and effective transmission map estimation can be achieved. The proposed color analysis module is based on the gray world assumption and analyzes the color characteristics of the input hazy image. Subsequently, the visibility restoration module uses the adjusted transmission map and the color-correlated information to repair the color distortion in variable scenes captured during inclement weather conditions. The experimental results demonstrate that our proposed method provides superior haze removal in comparison to the previous method through qualitative and quantitative evaluations of different scenes captured during various weather conditions.