An Optimal Weight Model for Single Image Super-Resolution

In this paper, a novel example-based super- resolution method is introduced. The objective is to estimate a high-resolution image from a single low- resolution image. By considering an image as a set of small image patches, our method is performed on each patch with the help of a given database of high and low-resolution image patch pairs. For each given low-resolution patch, its high-resolution version is considered as a sparse positive linear combination of the high-resolution patches from the database. The coefficients of this combination are referred to as the weights, and an optimal weight model is proposed to find this combination such that the high-resolution patch is consistent with the low- resolution patch while being similar to the best candidate high-resolution patches from the database. Experimental results show the good performance of our method over some state-of-the-art methods and confirm the efficiency of the proposed method.

[1]  Kwang In Kim,et al.  Example-Based Learning for Single-Image Super-Resolution , 2008, DAGM-Symposium.

[2]  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.

[3]  William T. Freeman,et al.  Example-Based Super-Resolution , 2002, IEEE Computer Graphics and Applications.

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

[5]  Chih-Yuan Yang,et al.  Exploiting Self-similarities for Single Frame Super-Resolution , 2010, ACCV.

[6]  Thomas Martin Deserno,et al.  Survey: interpolation methods in medical image processing , 1999, IEEE Transactions on Medical Imaging.

[7]  Michael Elad,et al.  Advances and challenges in super‐resolution , 2004, Int. J. Imaging Syst. Technol..

[8]  Michal Irani,et al.  Motion Analysis for Image Enhancement: Resolution, Occlusion, and Transparency , 1993, J. Vis. Commun. Image Represent..

[9]  Hong Chang,et al.  Super-resolution through neighbor embedding , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[10]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[11]  A. J. Shah,et al.  Image super resolution-A survey , 2012, 2012 1st International Conference on Emerging Technology Trends in Electronics, Communication & Networking.

[12]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[13]  Michael Elad,et al.  Sparse and Redundant Representations - From Theory to Applications in Signal and Image Processing , 2010 .

[14]  Peter J. Huber,et al.  Robust Statistics , 2005, Wiley Series in Probability and Statistics.

[15]  Marie Luong,et al.  Medical image denoising using Kernel Ridge Regression , 2011, 2011 18th IEEE International Conference on Image Processing.

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

[17]  Stephen Lin,et al.  Super resolution using edge prior and single image detail synthesis , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Moon Gi Kang,et al.  Super-resolution image reconstruction: a technical overview , 2003, IEEE Signal Process. Mag..

[19]  Simon K. Warfield,et al.  Robust Super-Resolution Volume Reconstruction From Slice Acquisitions: Application to Fetal Brain MRI , 2010, IEEE Transactions on Medical Imaging.