Image Deblurring Using Dark Channel Prior Liang Zhang (lzhang432)
Outline Motivation Solutions Application Future Work Reference Dark Channel Model Optimization Application Future Work Reference
Motivation Recover Blur Image Photos are taken everyday (mobile phone, digital camera, GoPros) Blur Images are undesirable Hard to reproduce the capture moment How to get deblur image without have to retake picture? Example of Blur Image
Motivation Blur Image = Sharp Image * Blur Kernel + Noise[8] Clear Image Blur Kernel Noise Blur Image = Sharp Image * Blur Kernel + Noise[8]
Motivation Blur Image = Sharp Image * Blur Kernel + Noise[8] Clear Image Blur Kernel Noise Blur Image = Sharp Image * Blur Kernel + Noise[8]
Outline Motivation Solutions Application Future Work Reference Dark Channel Model Optimization Application Future Work Reference
Convolution Convolution: Weighted average of a patch 50 10 120 30 180 120 30 180 25 90 60 2 20 75 3 80 15 5 12 300 200 150 1/9 Blur Kernel Clear Image
Convolution Convolution: Weighted average of a patch 50 10 120 30 180 120 30 180 25 90 60 2 20 75 3 80 15 5 12 300 200 150 1/9 45 Blur Kernel Clear Image
Convolution Dark Channel: Convolution: >= The lowest value among a patch Convolution: Weighted average of a patch 50 10 120 30 180 25 90 60 2 20 75 3 80 15 5 12 300 200 150 1/9 45 >= Blur Kernel Clear Image
Convolution and Dark Channel Image Dark channel of blurred image are less sparse than the dark channel of sharp image Dark Channel Clear Image Blurred Image
Deblur Approach Image Dark Channel Clear Image Blurred Image
Outline Motivation Solutions Application Future Work Reference Dark Channel Model Optimization Application Future Work Reference
Model Based of fact that the dark channel of sharp image have more number of zero-intensity pixels [8]
Model Based of fact that the dark channel of sharp image have more number of zero-intensity pixels [8] Data Fitting
Model Blur Kernel Regularization Based of fact that the dark channel of sharp image have more number of zero-intensity pixels [8] Blur Kernel Regularization
Model Gradients of Image Sparsity Based of fact that the dark channel of sharp image have more number of zero-intensity pixels [8] Gradients of Image Sparsity
This term is used to measure the sparsity of dark channel Model Based of fact that the dark channel of sharp image have more number of zero-intensity pixels [8] L0 norm Non-linear min operator This term is used to measure the sparsity of dark channel
Non-linear Operation D(I) = MI M D(I): vectorized of D(I) M: indicator matrix of dark channel I: vectorized latent image [8] M Latent Image Dark Channel
Non-linear Operation D(I) = MI M D(I): vectorized of D(I) M: indicator matrix of dark channel I: vectorized latent image [8] M Latent Image Dark Channel
Non-linear Operation D(I) = MI M D(I): vectorized of D(I) M: indicator matrix of dark channel I: vectorized latent image [8] M Latent Image Dark Channel
Blur Kernel and Clear Image Dark Channel of Latent Image Blur Kernel Clear Image
Outline Motivation Solutions Application Future Work Reference Dark Channel Model Optimization Application Future Work Reference
Application Run with paper’s data set
Application Run with paper’s data set
Application Run with our own data set (blur images are download from google)
Outline Motivation Solutions Application Future Work Reference Dark Channel Model Optimization Application Future Work Reference
Future Work Parallel computing implementation to accelerate deblur computing Implement the dark channel on mobile phone Improve the dark channel methods
Outline Motivation Solutions Application Future Work Reference Dark Channel Model Optimization Application Future Work Reference
Reference [1] L.B.Lucy. An iterative technique for the rectification of observed distributions. Astronomy Journal, 79(6):745-754, 1974. [2] T, Chan anc C.Wong. Total variation blind deconvolution.. IEEE TIP, 7(3):370-375, 1998. [3] R.Fergus, B.Singh, A.Hertzmann, S.T.Roweis, and W.T.Freeman. Removing camera shake from a single photograph. ACM SIGGRAPH, 25(3):787-794, 2006.
Reference [4] Y.Hacohen, E. Shechtman, and D.Lischinski. Deblurring by example using dense correspondence. In ICCV, pages2384-2391, 2013 [5] D.Krishnan, T.Tay, and R.Fergus. Blind deconvolution using a normalized sparsit measure. In CVPR, pages2657-2664, 2011 [6] T.Michaeli and M.Irani Blind deblurring using internal patch recurrence. In ECCV, pages783-798, 2014
Reference [7] K.He, J.Sun, and X.Tang Single image haze removal using dark channel prior. In CVPR, pages1956-1963, 2009 [8] Jinshan Pan, Deqing Sun, Hanspeter Pfister, and Ming-Hsuan Yang Blind Image Deblurring Using Dark Channel Prior. In CVPR, 2016 [9]https://sites.google.com/site/jspanhomepage/ [10]https://www.youtube.com/watch?v=dl1_592iDUY [11]https://www.google.com/search?q=blur+image&espv=2&source=lnms&tbm=isch&sa=X&ved=0ahUKEwjfntK7vrzTAhUo_IMKHRnfAm4Q_AUIBigB&biw=1074&bih=709&dpr=1
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