Paper Info
Title
Combined self-learning based single-image super-resolution and dual-tree complex wavelet transform denoising for medical images.
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.
Year
DOI
Venue
2016
10.1117/12.2207440
Proceedings of SPIE
Keywords
Field
DocType
Self-learning,sparse representation,super-resolution,denoising,discrete wavelet transform,dual-tree complex wavelet transform,medical imaging analysis,image processing
Computer vision,Computer science,Bicubic interpolation,Sparse approximation,Support vector machine,Image processing,Artificial intelligence,Discrete wavelet transform,Associative array,Complex wavelet transform,Wavelet transform
Conference
Volume
ISSN
Citations 
9784
0277-786X
1
PageRank 
References 
Authors
0.36
9
6
Name
Order
Citations
PageRank
Guang Yang1518.05
Xujiong Ye229922.78
Greg G. Slabaugh3104.91
J. Keegan410011.94
Raad Mohiaddin552540.16
David N. Firmin6498.71