@article{
 title = {Combined self-learning based single-image super-resolution and dual-tree complex wavelet transform denoising for medical images},
 type = {article},
 year = {2016},
 keywords = {denoising,discrete wavelet transform,dual-tree,self-learning,sparse representation,super-resolution},
 pages = {97840L},
 volume = {9784},
 publisher = {International Society for Optics and Photonics},
 id = {4fb5240a-0878-3b41-b5f2-bd63c93a248b},
 created = {2024-01-13T08:14:16.745Z},
 file_attached = {true},
 profile_id = {d5b9f595-4104-33d2-b640-e84dee1b7ded},
 last_modified = {2024-01-13T08:18:06.532Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {yang_combined_2016},
 source_type = {inproceedings},
 private_publication = {false},
 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.},
 bibtype = {article},
 author = {Yang, Guang and Ye, Xujiong and Slabaugh, Greg and Keegan, Jennifer and Mohiaddin, Raad and Firmin, David},
 doi = {10.1117/12.2207440},
 journal = {Medical Imaging 2016: Image Processing},
 number = {March 2016}
}

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