IMAGE RESTORATION BY MIXTURE MODELLING OF AN OVERCOMPLETE LINEAR REPRESENTATION

被引：2

作者：

Mancera, L. ^{[1
]}

Babacan, S. Derin ^{[2
]}

Molina, R. ^{[1
]}

Katsaggelos, A. K. ^{[2
]}

机构：

[1] Univ Granada, Dept Ciencias Computac eIA, E-18071 Granada, Spain

[2] Northwestern Univ, Dept Elect Engn & Comp Sci, Evanston, IL 60208 USA

来源：

2009 16TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-6 | 2009年

关键词：

Image restoration; linear representations; sparsity; hard-thresholding; overcomplete wavelets; PRIORS;

D O I：

10.1109/ICIP.2009.5413795

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a new image restoration method based on modelling the coefficients of an overcomplete wavelet response to natural images with a mixture of two Gaussian distributions, having non-zero and zero mean respectively, and reflecting the assumption that this response is close to be sparse. Including the observation model, the resulting procedure iterates between image reconstruction from the hard-thresholding of the response to the current estimate and a fast blur compensation step. Results indicate that our method compares favorably with current wavelet-based restoration methods.

引用

页码：3949 / +

页数：2

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