Combined self-learning based single-image super-resolution and dual-tree complex wavelet transform denoising for medical images

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.

[1]  Truong Q. Nguyen,et al.  Image Superresolution Using Support Vector Regression , 2007, IEEE Transactions on Image Processing.

[2]  Chia-Hung Yeh,et al.  Self-Learning-Based Low-Quality Single Image Super- Resolution , 2013 .

[3]  Thomas S. Huang,et al.  Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.

[4]  Thomas B. Moeslund,et al.  Super-resolution: a comprehensive survey , 2014, Machine Vision and Applications.

[5]  Truong Q. Nguyen,et al.  Novel Example-Based Method for Super-Resolution and Denoising of Medical Images , 2014, IEEE Transactions on Image Processing.

[6]  Narendra Ahuja,et al.  Super-Resolution Using Sub-Band Self-Similarity , 2014, ACCV.

[7]  Michal Irani,et al.  Super-resolution from a single image , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[8]  Chih-Yuan Yang,et al.  Single-Image Super-Resolution: A Benchmark , 2014, ECCV.

[9]  Guillermo Sapiro,et al.  Online Learning for Matrix Factorization and Sparse Coding , 2009, J. Mach. Learn. Res..

[10]  Narendra Ahuja,et al.  Single image super-resolution from transformed self-exemplars , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  D. Yeung,et al.  Super-resolution through neighbor embedding , 2004, CVPR 2004.

[12]  Thomas S. Huang,et al.  Coupled Dictionary Training for Image Super-Resolution , 2012, IEEE Transactions on Image Processing.

[13]  R. Cohen Signal Denoising Using Wavelets Project Report , 2012 .

[14]  Richard Baraniuk,et al.  The Dual-tree Complex Wavelet Transform , 2007 .

[15]  Yu-Chiang Frank Wang,et al.  A Self-Learning Approach to Single Image Super-Resolution , 2013, IEEE Transactions on Multimedia.

[16]  Heung-Yeung Shum,et al.  Fundamental limits of reconstruction-based superresolution algorithms under local translation , 2004 .

[17]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.