Education, Science, Technology, Innovation and Life
Open Access
Sign In

A Low Distortion Image Defogging Method Based on Histogram Equalization in Wavelet Domain

Download as PDF

DOI: 10.23977/jipta.2020.31001 | Downloads: 9 | Views: 467


Ruobing Wu 1, Linhan Gong 1, Jiaqing Liu 1, Xun Zhou 1


1 University of International Relations, Beijing, 100091, China

Corresponding Author

Ruobing Wu


With the development of computer vision, the requirement of image sharpness in production is increasing. Fog, as a common weather phenomenon, will affect the image quality, so it is necessary to defog the image. In the image defogging technology, the image enhancement method based on non-physical model represented by histogram equalization has the advantage of its relatively mature and simple algorithm, but the disadvantage is that, because of the enhancement effect on the image on the global scale, defogging the detail information will also affect the contour information, resulting in distortion.In this paper, an image defogging method with low distortion based on the histogram equalization in the wavelet domain is proposed, which uses the nondestructive and reversible properties of the integer wavelet transform to separate the detail information and contour information of the image. We equalize the histogram generated by the low-frequency coefficient of the image extracted by the integer wavelet transform, then carry out the inverse integer wavelet transform, and fuse the processed low-frequency coefficient with the high-frequency coefficient of the original image to generate the defogging image. Experimental results show that, compared with the common physical model and non-physical model algorithms, the characteristic of our method is that it has low distortion, on the basis of obvious fog removal effect, it retains the contour completely and meets the application requirements.


image defogging, image dehazing, integer wavelet transform, histogram equalization, low distortion, image enhancement


Ruobing Wu, Linhan Gong, Jiaqing Liu and Xun Zhou. A Low Distortion Image Defogging Method Based on Histogram Equalization in Wavelet Domain. Journal of Image Processing Theory and Applications (2020) Vol. 3: 1-10. DOI:


[1] Lu wenjun, li cong-li, lu wei, et al. Classification of foggy image scenes based on codebook [C]. // China society of image graphics. Proceedings of the 17th national conference on image graphics. 2014: 477-482.
[2] TAREL J P. Fast visibility restoration from a single color or gray level image [C]. Proceedings of IEEE Conference on International Conference on Computer Vision, 2009, 10: 20-28.
[3] HE K M, SUN J, TANG X O. Single image haze removal using Dark Channel Prior [C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, USA: IEEE. 2009: 1956-1963.
[4] Zhao hongyu. Research on foggy image sharpening technology [D]. Beijing: Beijing university of technology, 2015.
[5] LAND E H. The Retinex [J]. American Scientist. 1964, 52 (2): 247-264.
[6] KIM T K, PAIK J K, KANG B S. Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering [J]. IEEE Transactions on Consumer Electronics, 1998, 44 (1): 82-87. 
[7] Pizer S M, Johnston R E, Ericksen J P, et al. Contrast-Limited Adaptive Histogram Equalization: Speed and Effectiveness [C]. Conference on Visualization in Biomedical Computing. 1990. 
[8] SEOW M J, ASARI V K. Ratio rule and homomorphic filter for enhancement of digital color image [J]. Neurocomputing, 2006, 69 (7): 954-958.
[9] FU J C, LIEN H C, WONG S T. Wavelet-based histogram equalization enhancement of gastric sonogram images [J]. Computerized Medical Imaging and Graphics, 2000, 24 (2): 59-68
[10] RUSSO F. An image enhancement technique combining sharpening and noise reduction [J]. IEEE Transactions on Instrumentation and Measurement, 2002, 51 (4): 824-828.
[11] Hui xianglong, wang shigang, huo haodai, et al. Pedestrian tracking occlusion processing method based on weighted brightness histogram [C]. // Chinese society of image graphics. Proceedings of the 17th national academic conference on image graphics. 2014: 390-394.
[12] Calderbank RC. Daubechies I. Sweldem W. etal. Wavelet Transforms That Map Integer To Integers [J]. Appl&CompHarm Anal, 1998, 5 (3): 332-369.
[13] Daubechies I. Sweldens W. Factoring Wavelet Transform into Lifting Step [J]. Technical Report, Bell Laboratories, Lucent Technologies, 1996.
[14] G. A. Woodell, D. J. Jobson, Z. Rahman Scene Context Dependency of Pattern Constancy of Time Series Imagery Visual Information Processing XVII [C] // Proc. SPIE 6978, 2008.
[15] Chun, Marvin M, Wolfe, Jeremy M. Visual Attention [M] // Blackwell Handbook of Sensation and Perception. Blackwell Publishing Ltd, 2008.
[16] hu anzhou. Study on subjective and objective consistent image perception quality evaluation method [D]. University of science and technology of China, 2014.
[17] Liu s q, wu l f, gong y, et al. Review of image quality evaluation [J]. China sci-tech paper online. 2011, 6 (7). 501-506. (in Chinese with English abstract) 
[18] Ke Gu, Guangtao Zhai, Xiaokang Yang, Wenjun Zhang. No-reference image quality assessment metric by combining free energy theory and structural degradation model [C]. 2013 IEEE International Conference on Multimedia and Expo (ICME), 2013. 1-6. 
[19] Reinhard, Erik, Mike Stark, Peter Shirley,James Ferwerda. "Photographic Tone Reproduction for Digital Images". ACM Transactions on Graphics (TOG), Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH), pp. 267-276. New York, NY: ACM Press, 2002.
[20] KIM J H, JANG W D, SIM J Y, et al. Optimized contrast enhancement for real-time image and video dehazing [J]. Journal of Visual Communication and Image Representation, 2013, 24 (3): 410-425
[21] zhang wei, Sun Ronghua xiao-can zhang. Based on the improved wavelet soft threshold method of SAR image denoising [J]. Journal of remote sensing information, 2004 (4): 4-6. DOI: 10.3969 / j.i SSN. 1000-3177.2004.04.002 
[22] Retinex Image Processing at NASA Langley Research Center Database [DB/OL].
[23] ITU-T Rec. BT. 500-13 (01.12): Methodology for the subjective assessment of the quality of television pictures [S]. 2013.
[24] ITU. Methodology for the subjective assessment of the quality of television pictures, ITU-RREC.BT.500-12 [S]. International Telecommunication Union, Geneva, Switzerland, 20

Downloads: 387
Visits: 20859

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.