Imperial College London
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ProfessorDavidFirmin

Faculty of Medicine, National Heart & Lung Institute

Emeritus Professor of Biomedical Imaging
 
 
 
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Contact

 

+44 (0)20 7351 8801d.firmin

 
 
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Location

 

Cardiovascular MR UnitRoyal Brompton Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Yang:2016:10.1117/12.2207440,
author = {Yang, G and Ye, X and Slabaugh, G and Keegan, J and Mohiaddin, R and Firmin, D},
doi = {10.1117/12.2207440},
publisher = {Society of Photo Optical Instrumentation Engineers},
title = {Combined self-learning based single-image super-resolution and dual-tree complex wavelet transform denoising for medical images},
url = {http://dx.doi.org/10.1117/12.2207440},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - 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.
AU  - Yang,G
AU  - Ye,X
AU  - Slabaugh,G
AU  - Keegan,J
AU  - Mohiaddin,R
AU  - Firmin,D
DO  - 10.1117/12.2207440
PB  - Society of Photo Optical Instrumentation Engineers
PY  - 2016///
TI  - Combined self-learning based single-image super-resolution and dual-tree complex wavelet transform denoising for medical images
UR  - http://dx.doi.org/10.1117/12.2207440
UR  - http://hdl.handle.net/10044/1/39585
ER  -
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