基于显著图的可变模板形态学去雾方法
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摘要
针对暗通道先验去雾中存在的光晕现象和天空区域颜色失真现象,提出了一种基于自适应可变形结构元(Adaptive deformable structuring element, ADSE)中值滤波结合灰度形态学重构精细化透射率的方法.该方法利用透射率与图像细纹理结构的无关性,由有雾图像的灰度图计算显著图(Salience map, SM),将SM作为导向图计算ADSE,用生成的ADSE对最小颜色通道图像进行自适应中值滤波运算;其次,以粗估计暗通道图像为标记图像,以自适应中值滤波后的图像作为模板图像进行灰度形态学重构运算,获得精细化暗通道图像,继而得到精细化透射率;最后,针对天空区域,引入最优化透射率方法,对天空和非天空区域做统一的运算得到最终透射率,完成图像去雾.本文算法对真实场景具有很好的去雾效果,同时,基于形态学的运算易于并行化和硬件实现.
The dehaze image based on dark channel prior presents the phenomena of halo effect and color distortion in sky region. In this paper, the median filtering with adaptive deformable structuring element(ADSE) and the morphological reconstruction are introduced to estimate the fine transmission. The transmission does not relate to fine texture, so the median filtering can be performed on the minimum channel with ADSEs. The ADSEs can be computed by the salience map(SM) which is computed by haze image. Then, the filtered image and dark channel image of the haze image are used as the mask and marker images, respectively. The mask and marker images are used to perform morphological reconstruction for fine dark channel image and fine transmission. Finally, the transmissions in nonsky and sky regions are fused by the optimized medium transmission method. The experiment results show that the proposed method can obtain good dehazing effect, especially in real-world images. The proposed algorithm is based on morphology operations, which is easy for parallel computing and hardware implementation.
The dehaze image based on dark channel prior presents the phenomena of halo effect and color distortion in sky region. In this paper, the median filtering with adaptive deformable structuring element(ADSE) and the morphological reconstruction are introduced to estimate the fine transmission. The transmission does not relate to fine texture, so the median filtering can be performed on the minimum channel with ADSEs. The ADSEs can be computed by the salience map(SM) which is computed by haze image. Then, the filtered image and dark channel image of the haze image are used as the mask and marker images, respectively. The mask and marker images are used to perform morphological reconstruction for fine dark channel image and fine transmission. Finally, the transmissions in nonsky and sky regions are fused by the optimized medium transmission method. The experiment results show that the proposed method can obtain good dehazing effect, especially in real-world images. The proposed algorithm is based on morphology operations, which is easy for parallel computing and hardware implementation.
引文
1 Narasimhan S G, Nayar S K. Vision and the atmosphere.International Journal of Computer Vision, 2002, 48(3):233-254
2 He K M, Sun J, Tang X O. Single image haze removal using dark channel prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12):2341-2353
3 Wu Di, Zhu Qing-Song. The latest research progress of image dehazing. Acta Automatica Sinica, 2015, 41(2):221-239(吴迪,朱青松.图像去雾的最新研究进展.自动化学报, 2015,41(2):221-239)
4 Stark J A. Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Transactions on Image Processing, 2000, 9(5):889-896
5 McCann J. Lessons learned from Mondrians applied to real images and color gamuts. In:Proceedings of the IS&T/SID7 th Color and Imaging Conference. Scottsdale, USA, 1999.1-8
6 Schechner Y Y, Narasimhan S G, Nayar S K. Instant dehazing of images using polarization. In:Proceedings of the2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Kauai, HI, USA:IEEE, 2001.I-325-I-332
7 Kopf J, Neubert B, Chen B, Cohen M, Cohen-Or D, Deussen O, et al. Deep photo:model-based photograph enhancement and viewing. ACM Transactions on Graphics, 2008, 27(5):Article No. 116
8 Narasimhan S G, Nayar S K. Contrast restoration of weather degraded images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(6):713-724
9 Narasimhan S G, Nayar S K. Chromatic framework for vision in bad weather. In:Proceedings of the 2000 IEEE Conference on Computer Vision and Pattern Recognition.Hilton Head Island, SC, USA:IEEE, 2000. 598-605
10 Nayar S K, Narasimhan S G. Vision in bad weather. In:Proceedings of the 7th IEEE International Conference on Computer Vision. Kerkyra, Greece:IEEE, 1999. 820-827
11 Zhu Q S, Mai J M, Shao L. A fast single image haze removal algorithm using color attenuation prior. IEEE Transactions on Image Processing, 2015, 24(11):3522-3533
12 Li Z G, Zheng J H. Edge-preserving decomposition-based single image haze removal. IEEE Transactions on Image Processing, 2015, 24(12):5432-5441
13 Caraffa L, Tarel J P. Markov random field model for single image defogging. In:Proceedings of the 2013 IEEE Intelligent Vehicles Symposium(IV). Gold Coast, QLD, Australia:IEEE, 2013. 994-999
14 Liu Hai-Bo, Yang Jie, Wu Zheng-Ping, Zhang Qing-Nian,Deng Yong. A fast single image dehazing method based on dark channel prior and Retinex theory. Acta Automatica Sinica, 2015, 41(7):1264-1273(刘海波,杨杰,吴正平,张庆年,邓勇.基于暗通道先验和Retinex理论的快速单幅图像去雾方法.自动化学报, 2015, 41(7):1264-1273)
15 Chen J, Chau L P. Heavy haze removal in a learning framework. In:Proceedings of the 2015 IEEE International Symposium on Circuits and Systems. Lisbon, Portugal:IEEE,2015. 1590-1593
16 Tang K T, Yang J C, Wang J. Investigating haze-relevant features in a learning framework for image dehazing. In:Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA:IEEE,2014. 2995-3002
17 Choi L, You J, Bovik A C. Referenceless prediction of perceptual fog density and perceptual image defogging.IEEE Transactions on Image Processing, 2015, 24(11):3888-3901
18 Cai B L, Xu X M, Jia K, Qing C M, Tao D C. DehazeNet:an end-to-end system for single image haze removal. IEEE Transactions on Image Processing, 2016,25(11):5187-5198
19 Chen Shu-Zhen, Ren Zhan-Guang, Lian Qiu-Sheng. Single image dehazing algorithm based on improved dark channel prior and guided filter. Acta Automatica Sinica, 2016, 42(3):455-465(陈书贞,任占广,练秋生.基于改进暗通道和导向滤波的单幅图像去雾算法.自动化学报, 2016, 42(3):455-465)
20 He K M, Sun J, Tang X O. Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence,2013, 35(6):1397-1409
21 Tarel J P, Hautiere N. Fast visibility restoration from a single color or gray level image. In:Proceedings of the 2009IEEE 12th International Conference on Computer Vision.Kyoto, Japan:IEEE, 2009. 2201-2208
22 Kim J H, Jang W D, Sim J Y, Kim C S. Optimized contrast enhancement for real-time image and video dehazing.Journal of Visual Communication and Image Representation, 2013, 24(3):410-425
23 Meng G F, Wang Y, Duan J Y, Xiang S M, Pan C H. Efficient image dehazing with boundary constraint and contextual regularization. In:Proceedings of the 2013 IEEE International Conference on Computer Vision. Sydney, NSW,Australia:IEEE, 2013. 617-624
24 Levin A, Lischinski D, Weiss Y. A closed-form solution to natural image matting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(2):228-242
25 Xiao C X, Liu M, Xiao D L, Dong Z, Ma K L. Fast closedform matting using a hierarchical data structure. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(1):49-62
26 Huang S C, Chen B H, Cheng Y J. An efficient visibility enhancement algorithm for road scenes captured by intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(5):2321-2332
27 Li X Y, Gu Y, Hu S M, Martin R R. Mixed-domain edgeaware image manipulation. IEEE Transactions on Image Processing, 2013, 22(5):1915-1925
28 Huang S C, Chen B H, Wang W J. Visibility restoration of single hazy images captured in real-world weather conditions. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(10):1814-1824
29 Lai Y H, Chen Y L, Chiou C J, Hsu C T. Single-image dehazing via optimal transmission map under scene priors.IEEE Transactions on Circuits and Systems for Video Technology, 2015, 25(1):1-14
30 Li Z G, Zheng J H, Zhu Z J, Yao W, Wu S Q. Weighted guided image filtering. IEEE Transactions on Image Processing, 2015, 24(1):120-129
31 Curic V, Hendriks C L, BorgeforsG. Salience adaptive structuring elements. IEEE Journal of Selected Topics in Signal Processing, 2012, 6(7):809-819
32 Wang Z, Bovik A C, Sheikh H R, Simoncelli E P. Image quality assessment:from error visibility to structural similarity. IEEE Transactions on Image Processing, 2004, 13(4):600-612
2 He K M, Sun J, Tang X O. Single image haze removal using dark channel prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12):2341-2353
3 Wu Di, Zhu Qing-Song. The latest research progress of image dehazing. Acta Automatica Sinica, 2015, 41(2):221-239(吴迪,朱青松.图像去雾的最新研究进展.自动化学报, 2015,41(2):221-239)
4 Stark J A. Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Transactions on Image Processing, 2000, 9(5):889-896
5 McCann J. Lessons learned from Mondrians applied to real images and color gamuts. In:Proceedings of the IS&T/SID7 th Color and Imaging Conference. Scottsdale, USA, 1999.1-8
6 Schechner Y Y, Narasimhan S G, Nayar S K. Instant dehazing of images using polarization. In:Proceedings of the2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Kauai, HI, USA:IEEE, 2001.I-325-I-332
7 Kopf J, Neubert B, Chen B, Cohen M, Cohen-Or D, Deussen O, et al. Deep photo:model-based photograph enhancement and viewing. ACM Transactions on Graphics, 2008, 27(5):Article No. 116
8 Narasimhan S G, Nayar S K. Contrast restoration of weather degraded images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(6):713-724
9 Narasimhan S G, Nayar S K. Chromatic framework for vision in bad weather. In:Proceedings of the 2000 IEEE Conference on Computer Vision and Pattern Recognition.Hilton Head Island, SC, USA:IEEE, 2000. 598-605
10 Nayar S K, Narasimhan S G. Vision in bad weather. In:Proceedings of the 7th IEEE International Conference on Computer Vision. Kerkyra, Greece:IEEE, 1999. 820-827
11 Zhu Q S, Mai J M, Shao L. A fast single image haze removal algorithm using color attenuation prior. IEEE Transactions on Image Processing, 2015, 24(11):3522-3533
12 Li Z G, Zheng J H. Edge-preserving decomposition-based single image haze removal. IEEE Transactions on Image Processing, 2015, 24(12):5432-5441
13 Caraffa L, Tarel J P. Markov random field model for single image defogging. In:Proceedings of the 2013 IEEE Intelligent Vehicles Symposium(IV). Gold Coast, QLD, Australia:IEEE, 2013. 994-999
14 Liu Hai-Bo, Yang Jie, Wu Zheng-Ping, Zhang Qing-Nian,Deng Yong. A fast single image dehazing method based on dark channel prior and Retinex theory. Acta Automatica Sinica, 2015, 41(7):1264-1273(刘海波,杨杰,吴正平,张庆年,邓勇.基于暗通道先验和Retinex理论的快速单幅图像去雾方法.自动化学报, 2015, 41(7):1264-1273)
15 Chen J, Chau L P. Heavy haze removal in a learning framework. In:Proceedings of the 2015 IEEE International Symposium on Circuits and Systems. Lisbon, Portugal:IEEE,2015. 1590-1593
16 Tang K T, Yang J C, Wang J. Investigating haze-relevant features in a learning framework for image dehazing. In:Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA:IEEE,2014. 2995-3002
17 Choi L, You J, Bovik A C. Referenceless prediction of perceptual fog density and perceptual image defogging.IEEE Transactions on Image Processing, 2015, 24(11):3888-3901
18 Cai B L, Xu X M, Jia K, Qing C M, Tao D C. DehazeNet:an end-to-end system for single image haze removal. IEEE Transactions on Image Processing, 2016,25(11):5187-5198
19 Chen Shu-Zhen, Ren Zhan-Guang, Lian Qiu-Sheng. Single image dehazing algorithm based on improved dark channel prior and guided filter. Acta Automatica Sinica, 2016, 42(3):455-465(陈书贞,任占广,练秋生.基于改进暗通道和导向滤波的单幅图像去雾算法.自动化学报, 2016, 42(3):455-465)
20 He K M, Sun J, Tang X O. Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence,2013, 35(6):1397-1409
21 Tarel J P, Hautiere N. Fast visibility restoration from a single color or gray level image. In:Proceedings of the 2009IEEE 12th International Conference on Computer Vision.Kyoto, Japan:IEEE, 2009. 2201-2208
22 Kim J H, Jang W D, Sim J Y, Kim C S. Optimized contrast enhancement for real-time image and video dehazing.Journal of Visual Communication and Image Representation, 2013, 24(3):410-425
23 Meng G F, Wang Y, Duan J Y, Xiang S M, Pan C H. Efficient image dehazing with boundary constraint and contextual regularization. In:Proceedings of the 2013 IEEE International Conference on Computer Vision. Sydney, NSW,Australia:IEEE, 2013. 617-624
24 Levin A, Lischinski D, Weiss Y. A closed-form solution to natural image matting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(2):228-242
25 Xiao C X, Liu M, Xiao D L, Dong Z, Ma K L. Fast closedform matting using a hierarchical data structure. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(1):49-62
26 Huang S C, Chen B H, Cheng Y J. An efficient visibility enhancement algorithm for road scenes captured by intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(5):2321-2332
27 Li X Y, Gu Y, Hu S M, Martin R R. Mixed-domain edgeaware image manipulation. IEEE Transactions on Image Processing, 2013, 22(5):1915-1925
28 Huang S C, Chen B H, Wang W J. Visibility restoration of single hazy images captured in real-world weather conditions. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(10):1814-1824
29 Lai Y H, Chen Y L, Chiou C J, Hsu C T. Single-image dehazing via optimal transmission map under scene priors.IEEE Transactions on Circuits and Systems for Video Technology, 2015, 25(1):1-14
30 Li Z G, Zheng J H, Zhu Z J, Yao W, Wu S Q. Weighted guided image filtering. IEEE Transactions on Image Processing, 2015, 24(1):120-129
31 Curic V, Hendriks C L, BorgeforsG. Salience adaptive structuring elements. IEEE Journal of Selected Topics in Signal Processing, 2012, 6(7):809-819
32 Wang Z, Bovik A C, Sheikh H R, Simoncelli E P. Image quality assessment:from error visibility to structural similarity. IEEE Transactions on Image Processing, 2004, 13(4):600-612