Generation of High Resolution Medical Images Using Super Resolution via Sparse Representation

Generation of high resolution (HR) of images is very important in image processing and computer vision applications such as: CT scan-chest, MRI, X-rays and Optical Coherence Tomography (OCT). In medical imaging field images are very frequently using for analyzing different type of disease symptoms. HR medical images are required for proper diagnosing but due to the hardware limitation it is very difficult. In this paper we propose a HR image generation technique based on image statistic research for single low resolution (LR) input image-using joint dictionary learning. The view of LR image is like down sampled version of HR image and its patches are considered to have a sparse representation with respect to an over complete dictionary. Sparse representation can be recovered under mild condition with compressed sensing perspective. This study explores the use of super resolution (SR), due to learning there is no need to aligned subpixels of different LR images. For validity of our research, we use this technique on OCT and Lungs images and train three dictionaries (i) using OCT images (ii) using Lungs images and (iii) using multiple different natural (MDN)-HR images. Images are produced with using MDN-HR and for both medical images and also used OCT images dictionary for OCT images and lungs images dictionary for lungs images. We compare our result with previous SR proposed technique-from all aspects dictionary learning approach have superiority. Proposed dictionary learning based technique produced enhanced and upsampled image.

[1]  Xuelong Li,et al.  Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression , 2012, IEEE Transactions on Image Processing.

[2]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[3]  Thomas S. Huang,et al.  Image super-resolution as sparse representation of raw image patches , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Joseph F. Murray,et al.  Learning Sparse Overcomplete Codes for Images , 2006, J. VLSI Signal Process..

[5]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

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

[7]  Luc Van Gool,et al.  Anchored Neighborhood Regression for Fast Example-Based Super-Resolution , 2013, 2013 IEEE International Conference on Computer Vision.

[8]  Zhe L. Lin,et al.  Fast Image Super-Resolution Based on In-Place Example Regression , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  David J. Field,et al.  Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.

[10]  Quan Pan,et al.  Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Jie Chen,et al.  Fusing Local Patterns of Gabor Magnitude and Phase for Face Recognition , 2010, IEEE Transactions on Image Processing.

[12]  William T. Freeman,et al.  Learning Low-Level Vision , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[13]  Nanning Zheng,et al.  Image hallucination with primal sketch priors , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[14]  Rajat Raina,et al.  Efficient sparse coding algorithms , 2006, NIPS.

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

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