Sparse coding for improved signal-to-noise ratio in MRI Razzaq,FA, Mohamed,S, Bhatti,A and Nahavandi,S 2014, Sparse coding for improved signal-to-noise ratio in MRI. In Loo,CK, Yap,KS, Wong,KW, Teoh,A and Huang,K (ed), Neural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part III, Springer Verlag, Germany, pp.258-265. Attached Files Name Description MIMEType Size Downloads Title Sparse coding for improved signal-to-noise ratio in MRI Author(s) Razzaq,FA Mohamed,S orcid.org/0000-0002-8851-1635 Bhatti,A orcid.org/0000-0001-6876-1437 Nahavandi,S orcid.org/0000-0002-0360-5270 Title of book Neural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part III Editor(s) Loo,CK Yap,KS Wong,KW Teoh,A Huang,K Publication date 2014 Series Lecture Notes in Computer Science; v.8836 Chapter number 32 Total chapters 83 Start page 258 End page 265 Total pages 8 Publisher Springer Verlag Place of Publication Germany Keyword(s) Additive White Gaussian Noise (AWGN) Magnetic Resonance imaging(MRI) Signalto Noise Ratio (SNR) Sparse Coding Science & Technology Technology Computer Science, Artificial Intelligence Computer Science, Information Systems Computer Science, Theory & Methods Computer Science Signal-to Noise Ratio (SNR) IMAGES CONTRAST Summary Magnetic Resonance images (MRI) do not only exhibit sparsity but their sparsity take a certain predictable shape which is common for all kinds of images. That region based localised sparsity can be used to de-noise MR images from random thermal noise. This paper present a simple framework to exploit sparsity of MR images for image de-noising. As, noise in MR images tends to change its shape based on contrast level and signal itself, the proposed method is independent of noise shape and type and it can be used in combination with other methods. Notes 21st International Conference, ICONIP 2014, Kuching, Malaysia, November 3-6, 2014. Proceedings ISBN 9783319126425 ISSN 0302-9743 1611-3349 Language eng Field of Research 080106 Image Processing Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category B1 Book chapter ERA Research output type B Book chapter Copyright notice ©2014, Springer Verlag Persistent URL http://hdl.handle.net/10536/DRO/DU:30071099 Document type: Book Chapter Collections: Institute for Intelligent Systems Research and Innovation (IISRI) ERA Postprints 2018 ERA Submission GTP Research Related Links Link Description   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 17 Mar 2015, 14:06:22 EST Wed, 18 Mar 2015, 04:15:20 EST Mon, 18 May 2015, 13:12:13 EST Mon, 18 May 2015, 13:12:47 EST Mon, 18 May 2015, 15:27:37 EST Mon, 18 May 2015, 16:29:33 EST Mon, 18 May 2015, 16:31:02 EST Fri, 12 Jun 2015, 12:14:56 EST Fri, 12 Jun 2015, 13:40:57 EST Fri, 06 Apr 2018, 09:46:00 EST Filtered Full Citation counts:   Cited 1 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 560 Abstract Views, 6 File Downloads  -  Detailed Statistics Created: Mon, 18 May 2015, 13:12:47 EST

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