A two-step approach to see-through bad weather for surveillance video quality enhancement

Abstract

Adverse weather conditions such as snow, fog or heavy rain greatly reduce the visual quality of outdoor surveillance videos. Video quality enhancement can improve the visual quality of surveillance videos providing clearer images with more details. Existing work in this area mainly focuses on quality enhancement for high resolution videos or still images, but few algorithms are developed for enhancing surveillance videos, which normally have low resolution, high noise and compression artifacts. In addition, for snow or rain conditions, the image quality of near-filed view is degraded by the obscuration of apparent snowflakes and raindrops, while the quality of far field view is degraded by the obscuration of fog-like snowflakes or raindrops. Very few video quality enhancement algorithms have been developed to handle both problems. In this paper, we propose a novel video quality enhancement algorithm for see-through snow, fog or heavy rain. The proposed algorithm has two major steps: 1. the near-field enhancement algorithm identifies obscuration pixels by snow or rain in the near-field view and removes these pixels as snowflakes or rain drops; different from state-of-the-art methods, the algorithm in this step can detect snowflakes on foreground object and background, and choose different methods to fill in the removed regions. 2. the far-field enhancement algorithm restores the image's contrast information not only to reveal more details in the far-field view but also to enhance the overall image's quality; in this step, the proposed algorithm adaptively enhances the global and local contrast, which is inspired on the human visual system, and accounts for the perceptual sensitivity to noise, compression artifacts, and the texture of image content. From our extensive testing, the proposed approach significantly improves the visual quality of surveillance videos by removing snow/fog/rain effects.