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» On Sparsity and Overcompleteness in Image Models
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TMI
2008
138views more  TMI 2008»
14 years 9 months ago
Dynamic Positron Emission Tomography Data-Driven Analysis Using Sparse Bayesian Learning
A method is presented for the analysis of dynamic positron emission tomography (PET) data using sparse Bayesian learning. Parameters are estimated in a compartmental framework usin...
Jyh-Ying Peng, John A. D. Aston, R. N. Gunn, Cheng...
ICANNGA
2007
Springer
191views Algorithms» more  ICANNGA 2007»
15 years 3 months ago
Novel Multi-layer Non-negative Tensor Factorization with Sparsity Constraints
In this paper we present a new method of 3D non-negative tensor factorization (NTF) that is robust in the presence of noise and has many potential applications, including multi-way...
Andrzej Cichocki, Rafal Zdunek, Seungjin Choi, Rob...
ICIP
2008
IEEE
15 years 11 months ago
Kalman filtered Compressed Sensing
We consider the problem of reconstructing time sequences of spatially sparse signals (with unknown and time-varying sparsity patterns) from a limited number of linear "incohe...
Namrata Vaswani
DCC
2007
IEEE
15 years 9 months ago
Spatial Sparsity Induced Temporal Prediction for Hybrid Video Compression
In this paper we propose a new motion compensated prediction technique that enables successful predictive encoding during fades, blended scenes, temporally decorrelated noise, and...
Gang Hua, Onur G. Guleryuz
TSP
2010
14 years 4 months ago
Double sparsity: learning sparse dictionaries for sparse signal approximation
Abstract--An efficient and flexible dictionary structure is proposed for sparse and redundant signal representation. The proposed sparse dictionary is based on a sparsity model of ...
Ron Rubinstein, Michael Zibulevsky, Michael Elad