PPT-The Analysis (Co-)Sparse Model

Author : stefany-barnette | Published Date : 2016-12-05

Origin Definition and Pursuit Michael Elad The Computer Science Department The Technion Israel Institute of technology Haifa 32000 Israel Joint work with Ron

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The Analysis (Co-)Sparse Model: Transcript

Origin Definition and Pursuit Michael Elad The Computer Science Department The Technion Israel Institute of technology Haifa 32000 Israel Joint work with Ron Rubinstein . Least Absolute Shrinkage via . The . CLASH. Operator. Volkan. Cevher. Laboratory. for Information . . and Inference Systems – . LIONS / EPFL. http://lions.epfl.ch . . & . Idiap. Research Institute. 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 . Aditya. Chopra and Prof. Brian L. Evans. Department of Electrical and Computer Engineering. The University of Texas at Austin. 1. Introduction. Finite Impulse Response (FIR) model of transmission media. Analysis . Sparse Models. Michael Elad. The Computer Science Department. The Technion – Israel Institute of technology. Haifa 32000, Israel. . SPARS11 Workshop:. . . Signal . Processing with Adaptive . Compressive. Sensing. Volkan . Cevher. volkan@rice.edu. Marco Duarte. Chinmay Hegde. Richard . Baraniuk. Dimensionality Reduction. Compressive sensing. non-adaptive measurements. Sparse Bayesian learning. The Computer Science Department. Technion. – Israel Institute of technology. Haifa 32000, Israel. Signal Modeling:. Joint work with. Jeremias . Sulam. Yaniv. Romano. Prof. Michael . Elad. 1. From . 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. Sabareesh Ganapathy. Manav Garg. Prasanna. . Venkatesh. Srinivasan. Convolutional Neural Network. State of the art in Image classification. Terminology – Feature Maps, Weights. Layers - Convolution, . Chao . Jia. and Brian L. Evans. The University of Texas at Austin. 12 Sep 2011. 1. Non-blind Image Deconvolution. Reconstruct natural image from blurred version. Camera shake; astronomy; biomedical image reconstruction. Ron Rubinstein. Advisor: Prof. Michael . Elad. October 2010. Signal Models. Signal models. . are a fundamental tool for solving low-level signal processing tasks. Noise Removal. Image Scaling. Compression. University . of Edinburgh – . Scotland. Remi. . Gribonval. ,. INRIA, . Rennes – . France. The Analysis . Cosparse. Model . for Signals and Images. Michael . Elad. , . Technion. ,. Haifa – . Israel. Sparsity. Testing over the Boolean Hypercube. Grigory. . Yaroslavtsev. http://grigory.us. Joint with Andrew Arnold (Waterloo), . Arturs. . Backurs. (MIT), Eric . Blais. (Waterloo) and Krzysztof . Reading Group Presenter:. Zhen . Hu. Cognitive Radio Institute. Friday, October 08, 2010. Authors: Carlos M. . Carvalho. , Nicholas G. Polson and James G. Scott. Outline. Introduction. Robust Shrinkage of Sparse Signals.

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