基于單幅圖像的快速去霧算法

1、1 仁 , , , 傮傦 亶 , 僔. . , , 仇 , , , , . , . 儈, . . , Narasimhan 1 , ; 2, Shwartz ; 3 ; 4 . , , . , Tan5 , 畝 973畝(畝 : 2010CB731800, 2009CB320602 、 (畝 : 61210012, 61021063, 61290324 . . Fattal 6 , . He7 傼 , , . , He . , , 5 600h400 5 7 (double processors of Pentium 4 and 1 GB memory, 6 512h512 35。(C+, a

2、 1.6 GHz Intel Pentium dual core processor, 7 600h400 10 20。(a 3.0 GHz Intel Pentium 4 Processor. Tarel8 , , , . 儈. 7 8 . 9 , , . , , . 10 , 7 僔. 11 , 仌 , . , , , 100084E-mail: zdh: , . . , . , . 傼 , , .: , , , Fast Haze Removal from a Single ImageQian Liu, Maoyin Chen, Donghua ZhouDepartment of Aut

3、omation, Tsinghua University, Beijing 100084, P. R. ChinaE-mail: zdhAbstract: Images of outdoor scenes show poor visibility in presence of haze. This results in poor performance of many outdoor images progressing systems. In this paper, we present a fast haze removal method using a single color or g

4、ray level image. Based on the analyses of physical-based model, we estimate airlight and atmospheric light utilizing the simple average filter. Our method is very simple and efficient and achieves real-time computation. Experiments demonstrate that our results achieve as good visibility as a few sta

5、te-of-art algorithms.Key Words: Haze removal, Real-time, Physical-based model, Visibility3780978-1-4673-5534-6/13/$31.00c 2013IEEE , 儈. , . 8 , 8 儈, , 8 , 8 . , . , , , . , .2 2.1 亶 , . , 1rd x rd x H x F x e A e (1, x ; H (Haze image; F (Haze-free image; r ; d ; A (Atmospheric light, , (x .(1, (rd

6、x F x e (Direct attenuation, ; (1rd x A e (Airlight (Atmospheric veil, . (1, (L x (1rd x A e,1rd x L x A e(2(1 1L x H x F x L x A (3, (H x (L x A , (F x . 2.2 (Medium transmission (t x 畝(rd x e , rd x t x e (4(1 (1H x F x t x A t x (5 (5,1A t x H x d (6 (6 仌 , ,min c c r g b M x H x (7 To o o A A A

7、A (8 (6 (1(oA t x M x d ,1o M x t x A t(9 (4, (t x d . (t x (x . (t x 0 , 0, (H x A , (t x 1 , , (H x F x . (t x , , . (t x .(9 11a a s s o o average M x M x average A A 21y x o a M y A s : (10, a s , (x : (x a a s s u . (t x , (t x , , a ave s M x average M x (11 1ave ave o oM x M x tx A A M (12 畝

8、, 01M d d .1G M , 1ave oM xtx A G (13 G , G , , , ; G , , . , av m G U (14 201325th Chinese Control and Decision Conference (CCDC3781, U 01av m U d d av m (M x . , av m 0 1, 0 1, av m (M x ; 0 255, (M x 255 . av m , G . , av m , , , . M , G , 0.9,min ,0.9av m G U (15 (9, (13 (15, max 1min ,0.9,1ave

9、av o o M x M x t x m A A U (16 (To o o L x L x L x L x , (2, (1(o o L x A t x , (Airlight min min ,0.9,o av ave L x m M x M x U (17 , , . , (H x , (M x (ave M x , (L x . ( 1 . 2.3 5 A , 僔 . 7 0.1% , A , , . A . , (10 0 1, , max(o ave A M x t , , : ,max(max (co c r g b A H x d ,max(max(max (cave o c

10、r g b M x A H x d d . ,o A ,max max 1max c o ave c r g b A H x M x H H (18, 01H d d . H , , , 0.5H ,11max max max 121c avec r g b A H x M x (19 , 亮 .3 (L x A , (F x 1H x L x F x L x A(20 僔 1 .1. 僔僔1 (H x . 2 (M x .,mincc r g bM x H x 3 (M x , (ave M x .aave sM x average M x4 (M x av m .5 (ave M x (L

11、 x .min min ,0.9,o av ave L x m M x M x U6 (ave M x (H x A .,1max max max 1112Tcavec r g bA Hx M x7 (F x .1H x L x F x L x A (H x , (H x (M x (o L x , o A1max max 2o ave A H x M x(21 (F x1o o oH x L x F x L x A(223782201325th Chinese Control and Decision Conference (CCDC, , , , 僔.1 (U =1.3, (H x , (

12、ave M x , (L x , (F x . 2 , U 傼 ,U 0.8, 1.2, 1.6 2.0.00.9av m U d d , U , , , ; 0.9av m U , U . , , av m , U ; av m . 1: 2: U 201325th Chinese Control and Decision Conference (CCDC3783 3: 4: 4 僂4.1 3 4 (U=1.3 . 3 4, , 7 , 8 . , 8 12 , 7 12 13.3, , , .4, , . , 7, 8 .4.2 8 . 8 3 4 2 3 . Matlab7.11 , :

13、 2.33GhzIntelCore2 (Q8200 CPU, 2GB , 64win7. (, 1/50( . 2 3 , 8. , , .2. ( /s 830.063 27.8330.032 5.2530.075 31.113. ( /s 840.033 39.7840.032 43.505 , , , 僔 , , , .3784201325th Chinese Control and Decision Conference(CCDC7 1 S. G. Narasimhan, S. K. Nayar, Contrast Restoration of Weather Degraded Ima

14、ges, IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol. 25, No. 8, 713-724, 2003. 2 S. Shwartz, E. Namer, Y. Schechner, Blind Haze Separation, in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 19841991, 2006. 3 S. G. Narasimhan, S. K. Nayar, Interactive Deweathering of an Imag

15、e Using Physical Models, in Proc. Workshop on Color and Photometric Methods in Computer Vision, 2003. 4 J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or,O. Deussen, M. Uyttendaele, D. Lischinski, Deep photo: Model-based photograph enhancement and viewing, ACM Trans. Graphics, Vol. 27, No. 5, 116:

16、1-116:10, 2008. 5 R. T. Tan, Visibility in Bad Weather from a Single Image, in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1-8, 2008. 6 R. Fattal, Single Image Dehazing, in Proc. ACM SIGGRAPH, 1-9, 2008. 9 8 Kaiming He, Jian Sun, Xiaoou Tang, Single Image Haze Removal Using Dark Channe

17、l Prior, in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1956-1963, 2009. J.-P. Tarel, N. Hautire, Fast Visibility Restoration from a Single Color or Gray Level Image, in Proc. IEEE Conf. Computer Vision, 22012208, 2009. Fang Li, Haoxian Wang, Xingpeng Mao, Yunlei Sun, Huiyao Song, Fast Single Image Defogging Algorithm, Computer Engineering and Design, Vol. 32, No. 12, 4129-4132, 2011. 10 Haoran Xu, Jianming Guo, Qing Liu, Lingli Ye, Fast Image Dehazing Using Improved Dark Channel Prior, in Proc. IEEE Conf. Information Science and Tech