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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