Objective measurement for image defogging algorithms

Abstract

Since there is lack of methodology to assess the performance of defogging algorithm and the existing assessment methods have some limitations, three new methods for assessing the defogging algorithm were proposed. One was using synthetic foggy image simulated by image degradation model to assess the defogging algorithm in full-reference way. In this method, the absolute difference was computed between the synthetic image with and without fog. The other two were computing the fog density of gray level image or constructing assessment system of color image from human visual perception to assess the defogging algorithm in no-reference way. For these methods, an assessment function was defined to evaluate algorithm performance from the function value. Using the defogging algorithm comparison, the experimental results demonstrate the effectiveness and reliability of the proposed methods.

Keywords

image defogging algorithm / image assessment / simulated foggy image / fog density / human visual perception

References

[1] TanR T. Visibility in bad weather from a single image [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2008, Anchorgae, USA, IEEE Engineering Society: 1-8

[2] TarelJ P, HautiereN. Fast visibility restoration from a single color or gray level image [C]. Proceedings of the 12th IEEE International Conference on Computer Vision, 2009, Kyoto, Japan, IEEE Engineering Society: 2201-2208

[3] HeK-m, SunJ, TangX-ou. Single image haze removal using dark channel prior [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009, New York, USA, IEEE Society: 1956-1963

[4] WangZ, BovikA C, SheikhH R, SimoncelliE P. Image quality assessment: From error visibility to structural similarity [J]. IEEE Trans Image Processing, 2004, 13(4): 600-612

[5] EskciogluA M, FisherP S. Image quality measures and their performance [J]. IEEE Trans Communication, 1995, 43(12): 2959-2965

[6] WangZ, SimoncelliE P. Reduced-reference image quality assessment using a wavelet-domain natural image statistic model [C]. Proceedings of SPIE’s 17th Annual Symposium on Electronic Image, 2005, Washington, SPIE Digital Library: 17-20

[7] CarnecM, CalletP L, BarbaD. Objective quality assessment of color images based on a generic perceptual reference [J]. Signal Processing: Image Communication, 2008, 23(4): 239-256

[8] HautiereN, TarelJ P, AubertD, DumontE. Blind contrast enhancement assessment by gradient ratioing at visible edges [J]. Image Analysis and Stereology Journal, 2006, 27(2): 87-95

[9] YuJ, XuD-b, LiaoQ-min. Image defogging: A survey [J]. Journal of Image and Graphics, 2011, 169: 1561-1576

[10] LiD-p, YuJ, XiaoC-bai. No-reference quality assessment method for defogged images [J]. Journal of Image and Graphics, 2011, 169: 1753-1757

[11] YaoB, HuangL, LiuC-ping. Research on an objective method to compare the quality of defogged images [C]. Proceedings of Chinese Conference on Pattern Recognition, 2009, New York, IEEE Society: 1-5

[12] BlackwellH R, BlackwellO M, BodmannH W. An analytic model for describing the influence of lighting parameters upon visual performance. CIE Report 19.2 [R]. Vienna: Central Burean of the International Commission on Illumination (CIE) [R], 1981

[13] AdrianW D I. Visibility of targets: Model for calculation [J]. Lighting Research and Technology, 1989, 21(4): 181-188

[14] NayarS K, NarasimhanS G. Vision in bad weather [C]. Proceedings of the IEEE International Conference on Computer Vision, 2002, New York, IEEE Engineering Society: 820-827

[15] YuJ, LiD-p, LiaoQ-min. Physics-based fast single image fog removal [J]. Acta Automatics Sinica, 2011, 37(2): 143-149

[16] RossumZ, NieuwenhuiznT. Multiple scattering of classical waves: Microscopy, mesoscopy and diffusion [J]. Reviews of Modern Physics, 1999, 71(1): 313-371

[17] YendrikhovskijS, BlommaertF, RidderH D. Perceptual optimal color reproduction [C]. Proceedings of SPIE, 1998, Washington, USA, SPIE Digital Library: 274-281

[18] HuangK-q, WangQ, WuZ-yang. Natural color image enhancement and evaluation algorithm based on human visual system [J]. Computer Vision and Image Understanding, 2006, 103(1): 52-63