PPT-Sparsity-Based Signal Models and the Sparse K-SVD Algorithm

Author : yoshiko-marsland | Published Date : 2017-11-03

Ron Rubinstein Advisor Prof Michael Elad October 2010 Signal Models Signal models are a fundamental tool for solving lowlevel signal processing tasks Noise Removal

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Sparsity-Based Signal Models and the Sparse K-SVD Algorithm: Transcript

Ron Rubinstein Advisor Prof Michael Elad October 2010 Signal Models Signal models are a fundamental tool for solving lowlevel signal processing tasks Noise Removal Image Scaling Compression. Volkan . Cevher. volkan.cevher@epfl.ch. Laboratory. for Information . . and Inference Systems - . LIONS. . http://lions.epfl.ch. Linear Dimensionality Reduction. Compressive sensing. non-adaptive measurements. Structured Sparsity Models. Volkan Cevher. volkan@rice.edu. Sensors. 160MP. 200,000fps. 192,000Hz. 2009 - Real time. 1977 - 5hours. Digital Data Acquisition. Foundation: . Shannon/Nyquist sampling theorem. Origin, Definition, Pursuit, Dictionary-Learning and Beyond. Michael Elad. The Computer Science Department. The Technion – Israel Institute of technology. Haifa 32000, Israel. . Mathematics & Image Analysis (MIA) 2012 Workshop – Paris . Sparsity. Authors:. Junzhou. Huang, Tong Zhang, . Dimitris. Metaxas. 1. Zhennan Yan. Introduction. Fixed set of . p. basis vectors where for each . j. . --> . Given a random observation , which depends on an underlying coefficient vector .. Origin, Definition, and Pursuit. Michael Elad. The Computer Science Department. The Technion – Israel Institute of technology. Haifa 32000, Israel. . *. *Joint . work with . Ron Rubinstein . onto convex sets. Volkan. Cevher. Laboratory. for Information . . and Inference Systems – . LIONS / EPFL. http://lions.epfl.ch . . joint work with . Stephen Becker. Anastasios. . Kyrillidis. ISMP’12. . Junzhou. Huang . Xiaolei. Huang . Dimitris. Metaxas . Rutgers University Lehigh University Rutgers University. Outline. Problem: Applications where the useful information is very less compared with the given data . Sabareesh Ganapathy. Manav Garg. Prasanna. . Venkatesh. Srinivasan. Convolutional Neural Network. State of the art in Image classification. Terminology – Feature Maps, Weights. Layers - Convolution, . . Junzhou. Huang . Xiaolei. Huang . Dimitris. Metaxas . Rutgers University Lehigh University Rutgers University. Outline. Problem: Applications where the useful information is very less compared with the given data . Dictionary Selection. for Sparse Representation. Volkan . Cevher. volkan.cevher@epfl.ch. Laboratory. for Information . . and Inference Systems - . LIONS. & . Idiap. Research Institute. . Joint work with . Yi Ma. 1,2. . Allen Yang. 3. John . Wright. 1. CVPR Tutorial, June 20, 2009. 1. Microsoft Research Asia. 3. University of California Berkeley. 2. University of Illinois . at Urbana-Champaign. Applications. Lecture 5. : Sparse optimization. Zhu Han. University of Houston. Thanks Dr. . Shaohua. Qin’s efforts on slides. 1. Outline (chapter 4). Sparse optimization models. Classic solvers and omitted solvers (BSUM and ADMM). Presented to you by :. ebraheem kashkosh . Samer Shahin . 1. A Technique For Removing Second-Order Light Effects From Hyperspectral Imaging Data. 2. schedule. quick intro. Review Second-Order Light problem. Afsaneh . Asaei. Joint work with: . Mohammad . Golbabaee. ,. Herve. Bourlard, . Volkan. . Cevher. φ. 21. φ. 52. s. 1. s. 2. s. 3. . s. 4. s. 5. x. 1. x. 2. φ. 11. φ. 42. 2. Speech . Separation Problem.

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