NDLI: Restoration of images and 3D data to higher resolution by deconvolution with sparsity regularization

Content Provider	IEEE Xplore Digital Library
Author	Yingsong Zhang Kingsbury, N.
Copyright Year	2010
Description	Author affiliation: Signal Processing & Communication Laboratory, Department of Engineering, University of Cambridge, U.K (Yingsong Zhang; Kingsbury, N.)
Abstract	Image convolution is conventionally approximated by the LTI discrete model. It is well recognized that the higher the sampling rate, the better is the approximation. However sometimes images or 3D data are only available at a lower sampling rate due to physical constraints of the imaging system. In this paper, we model the under-sampled observation as the result of combining convolution and subsampling. Because the wavelet coefficients of piecewise smooth images tend to be sparse and well modelled by tree-like structures, we propose the L0 reweighted-L2 minimization (L0RL2 ) algorithm to solve this problem. This promotes model-based sparsity by minimizing the reweighted L2 norm, which approximates the L0 norm, and by enforcing a tree model over the weights. We test the algorithm on 3 examples: a simple ring, the cameraman image and a 3D microscope dataset; and show that good results can be obtained.
Starting Page	1685
Ending Page	1688
File Size	179893
Page Count	4
File Format	PDF
ISBN	9781424479924
ISSN	15224880
e-ISBN	9781424479948
e-ISBN	9781424479931
DOI	10.1109/ICIP.2010.5653189
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-26
Publisher Place	Hong Kong
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Three dimensional displays Wavelet transforms Spline Hidden Markov models Noise Image restoration Deconvolution regularization deconvolution sparsity L0 norms
Content Type	Text
Resource Type	Article

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