融合超像素的黄金暗通道单幅图像去雾方法

doi:10.3969/j.issn.1001-2400.2017.05.025

Abstract

Abstract:

Fog and haze can cause serious image degradation. In the light of the limitations of dark channel theory, the fog concentration is supposed to be constant locally, and the effectiveness of dark channel prior decays exponentially as the depth increases. Based on this, we propose a superpixels-based golden dark channel algorithm for single image fog removal. Small regions are obtained by superpixels in which the fog concentration and the depth remain constant. The golden dark channel is computed in these regions. The resulting transmittance remains constant and is finer and more precise. This method can suppress the “Halo effect” which occurs in depth mutation. Moreover, An iterative strategy is employed to gradually reduce the overall density of fog, making the residual amount of fog satisfy the golden section after each iteration. Further, the golden section is used to simplify the tolerance value and deal with the color cast problems in the sky region where the depth is infinity. Experimental results show that the algorithm can effectively improve the image of visibility, and performs even better when the fog concentration is greater.

Key words: image defogging, atmospheric scattering model, superpixels, golden dark channel, transmittance

WANG Yunfei;LIU Huawei;ZHAO Boxin. Superpixels-based golden dark channel for single image fog removal[J].Journal of Xidian University, 2017, 44(5): 147-152.

References

［1］ CHOI L K, YOU J, BOVLK A C. Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging［J］. IEEE Transactions on Image Processing, 2015, 24(11): 3888-3901.
［2］ TAN R T. Visibility in Bad Weather from a Single Image［C］//Proceeding of the 26th IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2008: 4587643.
［3］ PAN X X, XIE F Y, JIANG Z G, et al. Haze Removal for a Single Remote Sensing Image Based on Deformed Haze Imaging Model［J］. IEEE Signal Processing Letters, 2015, 22(10): 1806-1810.
［4］ LI Z, ZHENG J. Edge-preserving Decomposition-based Single Image Haze Removal［J］. IEEE Transactions on Image Processing, 2015, 24(12): 5432-5441.
［5］ ANCUTI, C O, ANCUTI C. Single Image Dehazing by Multi-scale Fusion［J］. IEEE Transactions on Image Processing, 2013, 22(8): 3271-3282.
［6］南栋, 毕笃彦, 马时平, 等. 基于景深约束的单幅雾天图像去雾算法［J］. 电子学报, 2015, 43(3): 500-504.
NAN Dong, BI Duyan, MA Shiping, et al. Single Image Dehazing Method Based on Scene Depth Constraint［J］. Acta Electroncia Sinica, 2015, 43(3): 500-504.
［7］胡伟, 袁国栋, 董朝, 等. 基于暗通道优先的单幅图像去雾新方法［J］. 计算机研究与发展, 2010, 47(12): 2132-2140.
HU Wei, YUAN Guodong, DONG Zhao, et al. Imporved Single Image Dehazing Using Dark Channel Prior［J］. Journal of Computer Research and Development, 2010, 47(12): 2132-2140.
［8］ FATTAL R. Single Image Dehazing［J］. ACM Transactions on Graphics, 2008, 27(3): 1-9.
［9］ KRATZ L, NISHINO K. Factorizing Scene Albedo and Depth from a Single Foggy Image［C］//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE, 2009: 1701-1708.
［10］ TAREL J P, HAUTIERE N. Fast Visibility Restoration from a Single Color or Gray Level Image［C］//Proceedings of the 12^th IEEE International Conference on Computer Vision . Piscataway: IEEE, 2009: 2201-2208.
［11］ HE K M, SUN J, TANG X O. Single Image Haze Removal Using Dark Channel Prior［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12): 2341-2353.
［12］ ZHU S, CAO D H, JIANG S X, et al. Fast Superpixel Segmentation by Iterative Edge Refinement［J］. Electronics Letters, 2015, 51(3): 230-232.
［13］ SHEN J B, DU Y F, WANG W G, et al. Lazy Random Walks for Superpixel Segmentation［J］. IEEE Transactions on Image Processing, 2014, 23(4): 1451-1462.
［14］ ACHANTA R, SHAJI A, SMITH K, et al. SLIC Superpixels Compared to State-of-the-art Superpixel Methods［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274-2281.
［15］ ARBELAEZ P, MAIRE M, FOWLKES C, et al. Contour Detection and Hierarchical Image Segmentation［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(5): 898-916.
［16］ NEUBERT P, PROTZEL P. Superpixel Benchmark and Comparison［DB/OL］. ［2016-01-20］. http: //www. tu-chemnitz. de/etit/proaut/forschung/superpixel. html.
［17］汪云飞, 毕笃彦, 刘华伟, 等. 一种局部受限的规则聚类超像素算法［J］. 西安电子科技大学学报, 2016, 43(3): 103-108.
WANG Yunfei, BI Duyan, LIU Huawei, et al. Locally-restricted Regular Clustering Superpixel Algorithm［J］. Journal of Xidian University, 2016, 43(3): 103-108.
［18］蒋建国, 侯天峰, 齐美彬. 改进的基于暗原色先验的图像去雾算法［J］. 电路与系统学报, 2011, 16(2): 7-12.
JIANG Jianguo, HOU Tianfeng, Qi Meibin. Improved Algorithm on Image Haze Removal Using Dark Channel Prior［J］. Journal of Circuits and Systems, 2011, 16(2): 7-12.
［19］ HE K M, SUN J, TANG X O. Guided Image Filtering［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397-1409.
［20］ SINGH G. Evaluation of Various Digital Image Fog Removal Algorithm［J］. International Journal of Advanced Research in Computer and Communication Engineering, 2014, 7(3): 7536-7540.
［21］李大鹏, 禹晶, 肖创柏. 图像去雾的无参考客观质量评测方法［J］. 中国图象图形学报, 2011, 16(9): 1753-1757.
LI Dapeng, YU Jing, XIAO Chuangbai. No-reference Quality Assessment Method for Defogged Images［J］. Journal of Image and Graphics, 2011, 16(9): 1753-1757.

[1]	BAO Wanting,WANG Junping,WEI Shulei,LI Yanbo,ZHOU Yong. Novel image defogging algorithm for sky region segmentation correction [J]. Journal of Xidian University, 2019, 46(2): 164-169.
[2]	WANG Yunfei;BI Duyan;LIU Huawei;LIU Ling;ZHAO Xiaolin. Locally-restricted regular clustering superpixel algorithm [J]. Journal of Xidian University, 2016, 43(3): 95-100.

Superpixels-based golden dark channel for single image fog removal

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 2

Metrics

Comments

Recommended 10