A two-step approach to see-through bad weather for surveillance video quality enhancement
A two-step approach to see-through bad weather for surveillance video quality enhancement
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DOI：
10.1007/s00138-012-0416-6
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发表时间：
2012-11-01
影响因子：
3.3
通讯作者：
Finn, Alan
中科院分区：
文献类型：
--
作者：
Jia, Zhen;Wang, Hongcheng;Finn, Alan
恶劣的天气条件，如雪，雾或大雨，大大降低了户外监控视频的视觉质量。视频质量增强可以提高监控视频的视觉质量，提供更清晰的图像和更多的细节，以更好地满足人类的感知需求，并提高视频分析性能。现有的研究主要集中在对高分辨率视频或静止图像的质量增强上，而对低分辨率、高噪声、压缩伪像的监控视频的增强算法研究较少。此外，对于雪或雨条件，近场视图的图像质量由于明显的雪花或雨滴的遮挡而降低，而远场视图的质量由于雾状雪花或雨滴的遮挡而降低。很少有视频质量增强算法被开发来处理这两个问题。本文提出了一种新的视频质量增强算法，用于透明的雪，雾或大雨。该算法不仅改善了视频监控的视觉感知体验，而且可以揭示更多的视频内容，以便更好地进行视频内容分析。该算法通过两步算法同时处理近场和远场的雪/雨效应：（1）近场增强算法识别出近场图像中被雪或雨遮挡的像素，并将这些像素作为雪花或雨滴去除;与现有技术的方法不同，我们在这一步中提出的算法可以检测前景物体或背景上的雪花，并应用不同的方法来填充去除的区域。(2)远场增强算法恢复图像的对比度信息，不仅揭示更多的细节在远场视图，但也提高整体图像的质量，在这一步中，该算法自适应地增强全局和局部的对比度，这是人类视觉系统的启发，并考虑到感知的敏感性噪声，压缩伪影，和图像内容的纹理。从我们广泛的测试，所提出的方法显着提高了视觉质量的监控视频，通过消除雪/雾/雨的影响。
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 to better meet human perception needs and also improve video analytics performance. Existing work in this area mainly focuses on the quality enhancement for high-resolution videos or still images, but few algorithms are developed for enhancing surveillance videos, which normally have low resolution, high noises and compression artifacts. In addition, for snow or rain conditions, the image quality of near-field view is degraded by the obscuration of apparent snowflakes or 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. Our algorithm not only improves human visual perception experiences for video surveillance, but also reveal more video contents for better video content analyses. The proposed algorithm handles both near-field and far-field snow/rain effects by proposed a two-step approach: (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 raindrops; different from state-of-the-art methods, our proposed algorithm in this step can detect snowflakes on foreground objects or background, and apply 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 noises, 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.