'Image denoising' presentation slideshows

Image Denoising and Beyond via Learned Dictionaries and Sparse Representations

*. * Joint work with M. Aharon A.M. Bruckstein O. Bryt D.L. Donoho J. Mairal M. Protter B. Matalon G. Sapiro J. Shtok J.L. Starck I. Yavneh M. Zibulevsky. Tel-Aviv University Applied Math Department Approximation Seminar June 26, 2008.

557 views • 55 slides

Bilateral Filtering for Images & Meshes

725 views • 44 slides

Active Random Fields

Active Random Fields. Adrian Barbu. The MAP Estimation Problem. Estimation problem: Given input data y, solve Example: Image denoising Given noisy image y , find denoised image x Issues Modeling : How to approximate ? Computing : How to find x fast?. Noisy image y.

295 views • 29 slides

Sparse and Redundant Representation Modeling for Image Processing

272 views • 25 slides

243 views • 24 slides

Independent Component Analysis & Blind Source Separation

411 views • 23 slides

Image restoration using digital inpainting and noise removal

414 views • 20 slides

Inverse Halftoning via Nonlocal Regularization

Inverse Halftoning via Nonlocal Regularization. Xin Li West Virginia University. This work is partially supported by NSF CCF-0914353. What is Inverse Halftoning?. halftoning. X: continuous-tone original. inverse halftoning. Y: halftoned (B/W). ^. X: continuous-tone estimated.

622 views • 18 slides

Presented by: Mingyuan Zhou Duke University, ECE Feb 22, 2013

Large Scale Variational Bayesian Inference for Structured Scale Mixture Models Young Jun Ko and Matthias Seeger ICML 2012. Presented by: Mingyuan Zhou Duke University, ECE Feb 22, 2013. Introduction. Natural image statistics exhibit hierarchical dependencies across multiple scales.

316 views • 14 slides

Digital Image Forensics

Digital Image Forensics. CS 365 By:- - Abhijit Sarang - Pankaj Jindal. Which of them are digitally manipulated?. How can we know?.

223 views • 11 slides

CVPR 2007 Sharing

CVPR 2007 Sharing. Xin (Frank) Fan 2007.6.29. What are popular?. Dynamic (video-based) Vision Two of twelve oral sessions motion segmentation and tracking Detection, Matching, Tracking Five poster sessions Optical Flow and Tracking (1,2,3) Body Tracking, Gait, and Gesture (1,2)

181 views • 7 slides

Math 3360: Mathematical Imaging

Math 3360: Mathematical Imaging. Lecture 14: Illustration of Isotropic diffusion & Anisotropic diffusion Image denoising through energy minimization. Prof. Ronald Lok Ming Lui Department of Mathematics, The Chinese University of Hong Kong. Isotropic diffusion. Original image.

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What is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images?

dim = argmax dim [var(pts dim )] split = median(pts dim ) L_pts = [pts dim < split] R_pts = [pts dim > split]. PCA Tree. axis = pca(pts).eigvec[0] curpts = project(pts, axis) split = median(curpts) L_pts = [curpts < split] R_pts = [curpts > split]. Ball Tree.

259 views • 1 slides

What is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images?

dim = argmax dim [var(pts dim )] split = median(pts dim ) L_pts = [pts dim < split] R_pts = [pts dim > split]. PCA Tree. axis = pca(pts).eigvec[0] curpts = project(pts, axis) split = median(curpts) L_pts = [curpts < split] R_pts = [curpts > split]. Ball Tree.

174 views • 1 slides