PPT-Single Image Super-Resolution Using Sparse Representation

Michael Elad The Computer Science Department The Technion Israel Institute of technology Haifa 32000 Israel MS45 Recent Advances in Sparse and Nonlocal Image Regularization Part III of III. CSL862 SSA form brPage 2br Computing Static Single Assignment SSA Form Overview What is SSA Advantages of SSA over usedef chains Flavors of SSA Dominance frontiers Inserting nodes Renaming the temporaries Translating out of SSA form R Cytron J Ferra 0. Representation. for Natural Image Deblurring. Speaker: Wei-Sheng Lai. Date: 2013/04/26. Outline. Introduction. Related work. L. 0. Deblurring. Conclusion. 2. 1. Introduction. Form . of image blur :. Compressed Sensing. Mobashir. . Mohammad. Aditya Kulkarni. Tobias Bertelsen. Malay Singh. Hirak. . Sarkar. Nirandika. . Wanigasekara. Yamilet Serrano . Llerena. Parvathy. . Sudhir. Introduction. Mobashir. STORM/PALM Image Processing Software. Eric Rees, Clemens Kaminski, Miklos Erdelyi, Dan Metcalf, Alex Knight. Laser Analytics Group. , . University of Cambridge . & . Biotechnology Group. , . National Physical Laboratory. Weihong Deng (. 邓伟洪. ). Beijing Univ. Post. & Telecom.(. 北京邮电大学. ) . 2. Characteristics of Face Pattern. The facial shapes are too similar, sometimes identical ! (~100% face detection rate, kinship verification). Nets. İlke Çuğu 1881739. NIPS 2014 . Ian. . Goodfellow. et al.. At a . glance. (. http://www.kdnuggets.com/2017/01/generative-adversarial-networks-hot-topic-machine-learning.html. ). Idea. . Behind. 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. (Paper ID: 2314). Vishwanath Saragadam,. . Aswin. . Sankaranarayanan. ,. Xin Li. 1. Compressive sensing. Solving underdetermined linear system of equations. Relies on sparsity of signal. Orthogonal Matching Pursuit. Michael . Elad. The Computer Science Department. The . Technion. – Israel Institute of technology. Haifa 32000, . Israel. David L. Donoho. Statistics Department Stanford USA. Adam Coates, . Honglak. Lee, Andrew Y. Ng. 2017/03/09. 1. Introduction. Feature learning/representation is a major topic . when processing unlabeled high-dimensional . data. For example, how to cluster images by recognizing the objects inside?. Eric Rees, Clemens Kaminski, Miklos Erdelyi, Dan Metcalf, Alex Knight. Laser Analytics Group. , . University of Cambridge . & . Biotechnology Group. , . National Physical Laboratory. Contents. Introduction. Object Recognition. Murad Megjhani. MATH : 6397. 1. Agenda. Sparse Coding. Dictionary Learning. Problem Formulation (Kernel). Results and Discussions. 2. Motivation. Given a 16x16(or . nxn. ) image . Parallelization of Sparse Coding & Dictionary Learning Univeristy of Colorado Denver Parallel Distributed System Fall 2016 Huynh Manh 11/15/2016 1 Contents Introduction to Sparse Coding Applications of Sparse Representation Bharath Subramanyam. Audio Signals. where T is the duration of the signal and s(t) is the amplitude.. is a continuous function..  . Audio Resolution. However the continuous waveform of the audio signal needs to be discretized when it needs to be stored digitally in a computer. .