(4) ω t(x) = 1 ω min Ω ( (I C (y))) min 0 < ω < C A C = 1 (5) ω (5) t transmission map tmap 1 4(a) t 4(a) t tmap RGB 2 (a) RGB (A), (B), (C)

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Download "(4) ω t(x) = 1 ω min Ω ( (I C (y))) min 0 < ω < C A C = 1 (5) ω (5) t transmission map tmap 1 4(a) 2. 3 2. 2 t 4(a) t tmap RGB 2 (a) RGB (A), (B), (C)"

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1 (MIRU2011) RGB transmission map Dehazing, transmission map,,,, 1. He Dehazing [3] Dehazing 2. Dehazing [1] [8] Robby He Dark Channel Prior [3] [1] Fattal 2. 1 Dehazing [2] He 1 [3] Dehazing [4], [5] I(x) = J(x)t(x) + A(1 t(x)) (1) 2 I J A t (1) J J I t A IS3-37 : 1111

2 (4) ω t(x) = 1 ω min Ω ( (I C (y))) min 0 < ω < C A C = 1 (5) ω (5) t transmission map tmap 1 4(a) t 4(a) t tmap RGB 2 (a) RGB (A), (B), (C) I i = α i F i + (1 α i )B i (6) 2 (a) RGB 2 (b) F B α RGB (6) 2 (c) 3(a) α i ai i + b i w i (7) 3(b) a = 1 F B b = B F B w J(α) = min J(α, a, b) (8) a,b RGB α a, b ( min I C (x) ) ( = t(x) min J C (x) ) + C {r,g,b} C {r,g,b} ( 1 t(x) ) A C J(α) = α T Lα (9) (2) L Matting Laplacian C RGB α Levin Matting Laplacian [9] (5) tmap t(x) ( min min y Ω(x) C {r,g,b} IC (y) ) tmap t(x) t, t = t(x) ( ( J C (y) )) + ( 1 t(x) ) A C (3) E(t) = t T Lt + λ(t t) T (t t) (10) min y Ω(x) min C {r,g,b} Ω y t 1 [3] 2 λ t t(x) = 1 min Ω ( (I C (y))) min C A C (4) (L + λu)t = λ t (11) min Ω (min C ( IC (y) )) 1 t(x) 0 A C U L λ tmap 4(b) IS3-37 : 1112

3 (a) (b) RGB (c) 2 RGB (a) (b) 3 (a) transmission map (tmap) 4 (b) tmap tmap 2. 4 A A A 2. 5 Dehazing t A (1) J (1) J(x) = I(x) A max(t(x), t 0 ) + A (12) t(x) 0 IS3-37 : 1113

4 (a) 3(a) (b) Dehazing 5 Dehazing Dehazing 3. 3 tmap t 0 = 0.1 t 0.1 5(b) Dehazing tmap tmap Dehazing J 1) tmap ID 3. 4 tmap tmap 1 Dehazing 2 3 tmap tmap 4 tmap 5 Dark Channel Prior (1) Dehazing Dehazing tmap ) tmap ID 2) ID Dehazing ID 1 1) Dehazing ) Dehazing ID [10] [12] 1 tmap IS3-37 : 1114

5 5. tmap ID tmap Antari Z1200 II 6 6 Dehazing tmap ω = 0.95 λ = SCOPE ICT t 0 = 0.1 ε = (LR030) (1) tmap 7 8 Dehazing [1] Robby T. Tan, Visibility in Bad Weather from a Single Image, Computer Vision and Pattern Recogni- tion, CVPR IEEE Conference on, (2) 9(a) [2] Raanan Fattal, Single Image Dehazing, ACM SIG- GRAPH 2008 papers, pp.72:1 72:9, [3] Kaiming He, Jian Sun, Xiaoou Tang, Single Image ID 9(b) Haze Removal Using Dark Channel Prior, Computer 9(a) Vision and Pattern Recognition, CVPR IEEE Conference on, pp , (b) [4] Y.Y. Schechner, S.G. Narasimhan and S.K. Nayar, Instant Dehazing of Images using Polarization, IEEE Conference on Computer Vision and Pattern (3) Recognition (CVPR), Vol.I, pp , Dec, (a) [5] Y.Y. Schechner, S.G. Narasimhan and S.K. Nayar, ID Polarization-Based Vision through Haze, Vol.42, No.3, pp , Jan, (b) [6] Peter Carr, Richard Hartley, Improved Single Image Dehazing Using Geometry, Digital Image Computing: Techniques and Applications, pp , (4) 11 11(a),(b) [7] S.G. Narasimhan and S.K. Nayar, Vision and the Atmosphere, International Journal on Computer Vision, Vol.48, No.3, pp , Jul, (a) ID 12(b) [8] Yong Du, Guindon, B. and Cihlar, J., Haze detection and removal in high resolution satellite image with wavelet analysis, IEEE Transactions on Geoscience and Remote Sensing, 40, 1, pp , [9] Anat Levin, Dani Lischinski, Yair Weiss, A Closed- (5) 13 Form Solution to Natural Image Matting, IEEE 13 (a) Transactions on Pattern Analysis and Machine Intelligence, pp , February, (b) [10], CVIM , pp , (6) 14 [11] Yuri Boykov, Olga Veksler, Ramin Zabih, Fast Approximate Energy Minimization via Graph Cuts, Dark Channel Prior IEEE Transactions on Pattern Analysis and Machine Intelligence, pp , November, [12] C. Rother, V. Kolmogorov, and A. Blake, grabcut : interactive foreground extraction using iterated graph cuts, ACM Trans. Graph., Vol.23(3), pp.309?-314, IS3-37 : 1115

6 6 7 tmap 8 Dehazing IS3-37 : 1116

7 (a) ID (b) 9 tmap (a) ID (b) 10 (a) 1 (b) IS3-37 : 1117

8 (a) ID (b) 12 2 (a) 1 (b) 2 13 (a) (b) 14 IS3-37 : 1118

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