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Sparse coding for improved signal-to-noise ratio in MRI

chapter
posted on 2014-01-01, 00:00 authored by F A Razzaq, Shady MohamedShady Mohamed, Asim BhattiAsim Bhatti, Saeid NahavandiSaeid Nahavandi
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.

History

Title of book

Neural Information Processing

Volume

8836

Series

Lecture Notes in Computer Science; v.8836

Chapter number

32

Pagination

258 - 265

Publisher

Springer Verlag

Place of publication

Germany

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783319126425

Language

eng

Notes

21st International Conference, ICONIP 2014, Kuching, Malaysia, November 3-6, 2014. Proceedings

Publication classification

B Book chapter; B1 Book chapter

Copyright notice

2014, Springer Verlag

Extent

83

Editor/Contributor(s)

C Loo, K Yap, K Wong, A Teoh, K Huang