Ramesh Kumar, Nandini and Xiang, Wei and Soar, Jeffrey 
ORCID: https://orcid.org/0000-0002-4964-7556
(2011)
A novel image compressive sensing method based on complex measurements.
In: DICTA 2011: International Conference on Digital Image Computing: Techniques and Application, 6-8 Dec 2011, Noosa, Australia.
Abstract
Compressive sensing (CS) has emerged as an efficient signal compression and recovery technique, that exploits the sparsity of a signal in a transform domain to perform sampling and stable recovery. The existing image compression methods have complex coding techniques involved and are also vulnerable to errors. In this paper, we propose a novel image compression and recovery scheme based on compressive sensing principles. This is an alternative paradigm to conventional image coding and is robust in nature. To obtain a sparse representation of the input, discrete wavelet transform is used and random complex Hadamard transform is used for obtaining CS measurements. At the decoder, sparse reconstruction is carried out using
compressive sampling matching pursuit (CoSaMP) algorithm.
We show that, the proposed CS method for image sampling and
reconstruction is efficient in terms of complexity, quality and is comparable with some of the existing CS techniques. We also demonstrate that our method uses considerably less number of random measurements.
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| Item Type: | Conference or Workshop Item (Commonwealth Reporting Category E) (Paper) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Faculty/School / Institute/Centre: | Historic - Faculty of Engineering and Surveying - Department of Electrical, Electronic and Computer Engineering (Up to 30 Jun 2013) |
| Faculty/School / Institute/Centre: | Historic - Faculty of Engineering and Surveying - Department of Electrical, Electronic and Computer Engineering (Up to 30 Jun 2013) |
| Date Deposited: | 26 Feb 2012 06:42 |
| Last Modified: | 12 Aug 2014 23:45 |
| Uncontrolled Keywords: | compressive sensing; image representation; CS reconstruction; CoSaMP; complex Hadamard transforms |
| Fields of Research (2008): | 09 Engineering > 0906 Electrical and Electronic Engineering > 090609 Signal Processing 08 Information and Computing Sciences > 0802 Computation Theory and Mathematics > 080201 Analysis of Algorithms and Complexity 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing |
| Fields of Research (2020): | 40 ENGINEERING > 4006 Communications engineering > 400607 Signal processing 46 INFORMATION AND COMPUTING SCIENCES > 4613 Theory of computation > 461399 Theory of computation not elsewhere classified 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing |
| Socio-Economic Objectives (2008): | E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences |
| Identification Number or DOI: | https://doi.org/10.1109/DICTA.2011.36 |
| URI: | http://eprints.usq.edu.au/id/eprint/20647 |
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