Adaptive image dehazing for bright areas based on global dark channel prior

被引：7

作者：

Deng L. ^{[1
]}

机构：

[1] Guangxi Colleges and Universities Key Laboratory of Unmanned Aerial Vehicle (UAV) Telemetry, Guilin University of Aerospace Technology, Guilin

来源：

Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2016年 / 24卷 / 04期

关键词：

Adaptive tolerance; Bright area; Fuzzy logic control; Global dark channel prior; Image dehazing;

D O I：

10.3788/OPE.20162404.0892

中图分类号：

学科分类号：

摘要：

An adaptive image dehazing algorithm based on global dark channel prior was proposed to solve the invalidation of original dark channel prior algorithm in bright areas and problems of block effect, Halo effect and higher computational complexity. In this method, the blocking operation was substituted by a global dark channel operation, and the fuzzy logic controller was used to estimate adaptively the threshold of bright areas and the adjustment factor of transmission. After the atmospheric light was estimated in non-bright areas, the miscalculated transmission in bright areas was corrected according to the adaptive tolerance. The algorithm was compared with three kinds of image restoration dehazing algorithms. Experiment results show that the algorithm shows a good subjective visual effect for dehazing images, and the objective evaluation criteria, image contrast, information entropy and average gradient are also superior in performance to those of the other algorithms compared. It concludes that the presented method effectively eliminates the distortion in bright areas and solve the above problems caused by blocking, and the visibility of dehazing image and the operating efficiency have been enhanced significantly. © 2016, Science Press. All right reserved.

引用

页码：892 / 901

页数：9

共 16 条

[1]

Tan R.T., Visibility in bad weather from a single image, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, (2008)

[2]

Fattal R., Single image dehazing, ACM Transactions on Graphics Los Angeles: ACM, 27, 3, (2008)

[3]

Tarel J.P., Hautiere N., Fast visibility restoration from a single color or gray level image, 2009 IEEE 12th International Conference on Computer Vision, pp. 2201-2208, (2009)

[4]

He K., Sun J., Tang X., Single image haze removal using dark channel prior, IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 12, pp. 2341-2353, (2011)

[5]

Jiang J.G., Hou T.F., Qi M.B., Improved algorithm on image haze removal using dark channel prior, Journal of Circuits and Systems, 16, 2, pp. 8-12, (2011)

[6]

Ji X.Q., Dai M., Yin C.L., Et al., Haze removal for aerial degraded images, Opt. Precision Eng., 19, 7, pp. 1659-1668, (2011)

[7]

Yu J., Li D.P., Liao Q.M., Physics-based fast single image fog removal, Acta Automatica Sinica, 37, 2, pp. 143-148, (2011)

[8]

Sun W., Li D.J., Liu H.J., Et al., Fast single image fog removal based on atmospheric scattering mode, Opt. Precision Eng., 21, 4, pp. 1040-1046, (2013)

[9]

He K., Sun J., Tang X., Guided image filtering, Computer Vision-ECCV 2010, pp. 1-14, (2010)

[10]

Wang W.X., Xiao X., Chen L.Q., Image dark channel prior haze removal based on minimum filtering and guided filtering, Opt. Precision Eng., 23, 7, pp. 2100-2108, (2015)

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