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 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.
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References
Narasimhan S.G., Nayar S.K.: Contrast restoration of weather degraded images. IEEE Trans. Pattern Anal. Mach. Intell. 25(6), 713–724 (2003)
Tan, R.T.: Visibility in bad weather from a single image. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008) (2008)
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. In: Proceedings of 2009 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) (2009)
Tarel, J.-P., Hautière, N.: Fast visibility restoration from a single color or gray level image. In: Proceedings of IEEE International Conference on Computer Vision (ICCV’09), Kyoto, Japan, pp. 2201–2208 (2009). http://www.perso.lcpc.fr/tarel.jean-philippe/publis/iccv09.html
Kratz, L., Nishino, K.: Factorizing scene albedo and depth from a single foggy image. In: Proceedings of 2009 IEEE 12th International Conference on Computer Vision, pp. 1701–1708 (2009)
Zhang, X., Li, H., Qi, Y., Leow, W.K., Ng, T.K.: Rain removal in video by combining temporal and chromatic properties. In: Proceedings of 2006 IEEE International Conference on Multimedia and Explosion, pp. 461–464 (2006)
Barnum, P., Narasimhan, S.G., Kanade, T.: Analysis of rain and snow in frequency space. Int. J. Comput. Vis. (2008)
Garg K., Nayar S.K.: Vision and rain. Int. J. Comput. Vis. 75(1), 1–25 (2007)
Gonzalez R.C., Woods R.E.: Digital Image Processing. Prentice Hall, New Jersey (2001)
Wang Q., Ward R.K.: Fast image/video contrast enhancement based on weighted thresholded histogram equalization. IEEE Trans. Consumer Electr. 53(2), 757–764 (2007)
Tian, Y., Wan, Q., Wu, F.: Local histogram equalization based on the minimum brightness error. In: Proceedings of the Fourth International Conference on Image and Graphics, pp. 58–61 (2007)
Dale-Jones R., Tjahjadi T.: A study and modification of the local histogram equalization algorithm. Pattern Recognit. 26(9), 1373–1381 (2007)
Golan, A., Levy, A.: Method of adaptive image contrast enhancement. US Patent 20070031055
Zhao, J., Lei, S.: Methods and systems for automatic digital image enhancement with local adjustment. US Patent 20070092137
Szeliski, R.S.: Locally adapted histogram equalization. US Patent 6650774
Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Academic Press Graphics Gems Series: Graphics Gems, vol. IV, pp. 474–485 (1994)
Reza Ali M.: Realization of the contrast limited adaptive histogram equalization (clahe) for real-time image enhancement. J. VLSI Signal Process. 38(1), 35–44 (2004)
Malm, H., Oskarsson, M., Warrant, E., Clarberg, P., Hasselgren, J., Lejdfors, C.: Adaptive enhancement and noise reduction in very low light-level video. In: Proceedings of IEEE 11th International Conference on Computer Vision, pp. 1–8 (2007)
Forsyth D.A., Ponce J.: Computer Vision: A Modern Approach. Prentice Hall, New Jersey (2003)
Roth, Stefan, Black, Michael, J.: Fields of experts: a framework for learning image priors. In: Proceedings of 2005 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 860–867 (2005)
Scognamiglio, G., Rizzi, A., Albani, L., Ramponi, G.: Sharpness enhancement. US Patent 6847738
Wu H.R., Yuen M.: A generalized block-edge impairment metric for video coding. IEEE Signal Process. Lett. 4(11), 317–320 (1997)
Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of the Sixth International Conference on Computer Vision (ICCV), pp. 839–846 (1998)
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Jia, Z., Wang, H., Caballero, R.E. et al. A two-step approach to see-through bad weather for surveillance video quality enhancement. Machine Vision and Applications 23, 1059–1082 (2012). https://doi.org/10.1007/s00138-012-0416-6
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DOI: https://doi.org/10.1007/s00138-012-0416-6