Journal
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
Volume 24, Issue 10, Pages 1814-1824Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2014.2317854
Keywords
Bad weather; dark channel prior; fog; haze; sandstorm; visibility restoration (VR)
Categories
Funding
- National Science Council of Taiwan [NSC 100-2628-E-027-012-MY3, NSC 102-2221-E-027-065]
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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 outdoor object recognition systems, obstacle detection systems, video surveillance systems, and intelligent transportation systems. In order to solve this problem, visibility restoration (VR) 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 VR techniques. This paper proposes a novel VR method that uses a combination of three major modules: 1) a depth estimation (DE) module; 2) a color analysis (CA) module; and 3) a VR module. The proposed DE module takes advantage of the median filter technique and adopts our 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 CA module is based on the gray world assumption and analyzes the color characteristics of the input hazy image. Subsequently, the VR 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 with the previous state-of-the-art method through qualitative and quantitative evaluations of different scenes captured during various weather conditions.
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