NDLI: Improving Inverse Wavelet Transform by Compressive Sensing Decoding with Deconvolution

Content Provider	IEEE Xplore Digital Library
Author	Dong Liu Xiaoyan Sun Feng Wu
Copyright Year	2009
Abstract	By virtue of compressive sensing (CS) that can recover sparse signals from a few linear and non-adaptive measurements, we propose an alternative decoding method for inverse wavelet transform when only partial coefficients are available. Classic CS decoding such as $l_1$-minimization indeed provides better reconstruction of sparse signals than inverse wavelet transform. Since many natural images are not sparse, we propose to further improve CS decoding from the Bayesian point of view. Specifically, as wavelet transform can be described as convolution, we present an iterative deconvolution method for CS decoding in the case of partial wavelet coefficients. Experimental results demonstrate the efficiency of our method. We conclude that such findings indicate promising applications in compression.
Starting Page	455
Ending Page	455
File Size	120777
Page Count	1
File Format	PDF
ISBN	9781424437535
ISSN	10680314
DOI	10.1109/DCC.2009.19
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Deconvolution Iterative decoding Data compression Sun Asia Image reconstruction Bayesian methods Convolution Iterative methods
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications

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Improving Inverse Wavelet Transform by Compressive Sensing Decoding with Deconvolution