A novel image compressive sensing method based on complex measurements
Paper
Paper/Presentation Title | A novel image compressive sensing method based on complex measurements |
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Presentation Type | Paper |
Authors | Ramesh Kumar, Nandini (Author), Xiang, Wei (Author) and Soar, Jeffrey (Author) |
Journal or Proceedings Title | Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA 2011) |
ERA Conference ID | 42717 |
Number of Pages | 5 |
Year | 2011 |
Place of Publication | Los Alamitos, CA. United States |
ISBN | 0769545882 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/DICTA.2011.36 |
Web Address (URL) of Paper | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6128678 |
Conference/Event | 2011 International Conference on Digital Image Computing: Techniques and Application (DICTA 2011) |
Digital Image Computing Techniques and Applications | |
Event Details | Digital Image Computing Techniques and Applications DICTA Rank B B B B B B B B |
Event Details | 2011 International Conference on Digital Image Computing: Techniques and Application (DICTA 2011) Parent International Conference on Digital Image Computing: Techniques and Applications Event Date 06 to end of 08 Dec 2011 Event Location 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 |
Keywords | compressive sensing; image representation; CS reconstruction; CoSaMP; complex Hadamard transforms |
ANZSRC Field of Research 2020 | 400607. Signal processing |
461399. Theory of computation not elsewhere classified | |
460306. Image processing | |
Public Notes | © 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. |
Byline Affiliations | Department of Electrical, Electronic and Computer Engineering |
School of Information Systems | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q1256/a-novel-image-compressive-sensing-method-based-on-complex-measurements
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