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Proceedings Paper

Combined self-learning based single-image super-resolution and dual-tree complex wavelet transform denoising for medical images
Author(s): Guang Yang; Xujiong Ye; Greg Slabaugh; Jennifer Keegan; Raad Mohiaddin; David Firmin
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Paper Abstract

In this paper, we propose a novel self-learning based single-image super-resolution (SR) method, which is coupled with dual-tree complex wavelet transform (DTCWT) based denoising to better recover high-resolution (HR) medical images. Unlike previous methods, this self-learning based SR approach enables us to reconstruct HR medical images from a single low-resolution (LR) image without extra training on HR image datasets in advance. The relationships between the given image and its scaled down versions are modeled using support vector regression with sparse coding and dictionary learning, without explicitly assuming reoccurrence or self-similarity across image scales. In addition, we perform DTCWT based denoising to initialize the HR images at each scale instead of simple bicubic interpolation. We evaluate our method on a variety of medical images. Both quantitative and qualitative results show that the proposed approach outperforms bicubic interpolation and state-of-the-art single-image SR methods while effectively removing noise.

Paper Details

Date Published: 21 March 2016
PDF: 7 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97840L (21 March 2016); doi: 10.1117/12.2207440
Show Author Affiliations
Guang Yang, Royal Brompton Hospital (United Kingdom)
Imperial College London (United Kingdom)
Xujiong Ye, Univ. of Lincoln (United Kingdom)
Greg Slabaugh, City Univ. London (United Kingdom)
Jennifer Keegan, Royal Brompton Hospital (United Kingdom)
Imperial College London (United Kingdom)
Raad Mohiaddin, Royal Brompton Hospital (United Kingdom)
Imperial College London (United Kingdom)
David Firmin, Royal Brompton Hospital (United Kingdom)
Imperial College London (United Kingdom)


Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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