Single image super resolution based on sparse representation and adaptive dictionary selection

An improved single image super resolution based on patch-wise sparse recovery is proposed in this paper. K-SVD is adopted to train a coupled dictionary. Besides, adaptive selection is proposed among dictionaries with different patch size. Simulation results show that the proposed approach provides good subjective quality and up to 0.4 dB PSNR improvement with significant time reduction.

[1]  Michael Elad,et al.  Fast and robust multiframe super resolution , 2004, IEEE Transactions on Image Processing.

[2]  Christopher M. Bishop,et al.  Bayesian Image Super-Resolution , 2002, NIPS.

[3]  Michael Elad,et al.  Dictionaries for Sparse Representation Modeling , 2010, Proceedings of the IEEE.

[4]  Michael Elad,et al.  From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images , 2009, SIAM Rev..

[5]  Stephen J. Wright,et al.  Computational Methods for Sparse Solution of Linear Inverse Problems , 2010, Proceedings of the IEEE.

[6]  Wenhan Yang,et al.  Sparse representation based super resolution using saliency and edge information , 2014, Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific.

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

[8]  Y. C. Pati,et al.  Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[9]  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.

[10]  Mehmet Türkan,et al.  Sparse approximation with adaptive dictionary for image prediction , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[11]  Qi Wang,et al.  Super-resolution via K-means sparse coding , 2013, 2013 International Conference on Wavelet Analysis and Pattern Recognition.

[12]  Guillermo Sapiro,et al.  Supervised Dictionary Learning , 2008, NIPS.

[13]  Xuelong Li,et al.  Learning Multiple Linear Mappings for Efficient Single Image Super-Resolution , 2015, IEEE Transactions on Image Processing.

[14]  Michael Elad,et al.  Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation , 2010, IEEE Transactions on Signal Processing.

[15]  Mei Han,et al.  Soft Edge Smoothness Prior for Alpha Channel Super Resolution , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Michael J. Black,et al.  Fields of Experts , 2009, International Journal of Computer Vision.

[17]  Russell C. Hardie,et al.  Joint MAP registration and high-resolution image estimation using a sequence of undersampled images , 1997, IEEE Trans. Image Process..

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

[19]  Geoffrey E. Hinton,et al.  Learning Sparse Topographic Representations with Products of Student-t Distributions , 2002, NIPS.

[20]  Lianwen Jin,et al.  A novel feature extraction method using Pyramid Histogram of Orientation Gradients for smile recognition , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[21]  Chi Fang,et al.  Generalized joint kernel regression and adaptive dictionary learning for single-image super-resolution , 2014, Signal Process..

[22]  Lei Zhang,et al.  Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization , 2010, IEEE Transactions on Image Processing.

[23]  Michael Elad,et al.  On Single Image Scale-Up Using Sparse-Representations , 2010, Curves and Surfaces.

[24]  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..

[25]  Andrew Zisserman,et al.  Representing shape with a spatial pyramid kernel , 2007, CIVR '07.

[26]  Zongliang Gan,et al.  Single Image Super Resolution Through Neighbor Embedding Based on Field of Experts , 2013 .

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