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.
Similar content being viewed by others
References
TAN R T. Visibility in bad weather from a single image [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Anchorgae, USA: IEEE Engineering Society, 2008: 1–8.
TAREL J P, HAUTIERE N. Fast visibility restoration from a single color or gray level image [C]// Proceedings of the 12th IEEE International Conference on Computer Vision. Kyoto, Japan: IEEE Engineering Society, 2009: 2201–2208.
HE Kai-ming, SUN Jian, TANG Xiao-ou. Single image haze removal using dark channel prior [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE Society, 2009: 1956–1963
WANG Z, BOVIK A C, SHEIKH H R, SIMONCELLI E P. Image quality assessment: From error visibility to structural similarity [J]. IEEE Trans Image Processing, 2004, 13(4): 600–612.
ESKCIOGLU A M, FISHER P S. Image quality measures and their performance [J]. IEEE Trans Communication, 1995, 43(12): 2959–2965.
WANG Zhou, SIMONCELLI E 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. Washington: SPIE Digital Library, 2005: 17–20.
CARNEC M, CALLET P L, BARBA D. Objective quality assessment of color images based on a generic perceptual reference [J]. Signal Processing: Image Communication, 2008, 23(4): 239–256.
HAUTIERE N, TAREL J P, AUBERT D, DUMONT E. Blind contrast enhancement assessment by gradient ratioing at visible edges [J]. Image Analysis and Stereology Journal, 2006, 27(2): 87–95.
YU Jing, XU Dong-bin, LIAO Qing-min. Image defogging: A survey [J]. Journal of Image and Graphics, 2011, 16(9): 1561–1576. (in Chinese)
LI Da-peng, YU Jing, XIAO Chuang-bai. No-reference quality assessment method for defogged images [J]. Journal of Image and Graphics, 2011, 16(9): 1753–1757. (in Chinese)
YAO Bo, HUANG Lei, LIU Chang-ping. Research on an objective method to compare the quality of defogged images [C]// Proceedings of Chinese Conference on Pattern Recognition. New York: IEEE Society, 2009: 1–5. (in Chinese)
BLACKWELL H R, BLACKWELL O M, BODMANN H 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.
ADRIAN W D I. Visibility of targets: Model for calculation [J]. Lighting Research and Technology, 1989, 21(4): 181–188.
NAYAR S K, NARASIMHAN S G. Vision in bad weather [C]// Proceedings of the IEEE International Conference on Computer Vision. New York: IEEE Engineering Society, 2002: 820–827.
YU Jing, LI Dang-peng, LIAO Qing-min. Physics-based fast single image fog removal [J]. Acta Automatics Sinica, 2011, 37(2): 143–149. (in Chinese)
ROSSUM Z, NIEUWENHUIZN T. Multiple scattering of classical waves: Microscopy, mesoscopy and diffusion [J]. Reviews of Modern Physics, 1999, 71(1): 313–371.
YENDRIKHOVSKIJ S, BLOMMAERT F, RIDDER H D. Perceptual optimal color reproduction [C]// Proceedings of SPIE. Washington, USA: SPIE Digital Library, 1998: 274–281.
HUANG Kai-qi, WANG Qiao, WU Zhen-yang. Natural color image enhancement and evaluation algorithm based on human visual system [J]. Computer Vision and Image Understanding, 2006, 103(1): 52–63.
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item: Projects(91220301, 61175064, 61273314) supported by the National Natural Science Foundation of China; Project(126648) supported by the Postdoctoral Science Foundation of Central South University, China; Project(2012170301) supported by the New Teacher Fund for School of Information Science and Engineering, Central South University, China
Rights and permissions
About this article
Cite this article
Guo, F., Tang, J. & Cai, Zx. Objective measurement for image defogging algorithms. J. Cent. South Univ. 21, 272–286 (2014). https://doi.org/10.1007/s11771-014-1938-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11771-014-1938-z