Single-image super-resolution reconstruction based on global non-zero gradient penalty and non-local Laplacian sparse coding

Methods based on sparse coding have been successfully used in single-image super-resolution reconstruction. However, they tend to reconstruct incorrectly the edge structure and lose the difference among the image patches to be reconstructed. To overcome these problems, we propose a new approach based on global non-zero gradient penalty and non-local Laplacian sparse coding. Firstly, we assume that the high resolution image consists of two components: the edge component and the texture component. Secondly, we develop the global non-zero gradient penalty to reconstruct correctly the edge component and the non-local Laplacian sparse coding to preserve the difference among texture component patches to be reconstructed respectively. Finally, we develop a global and local optimization on the initial image, which is composed of the reconstructed edge component and texture component, to remove possible artifacts. Experimental results demonstrate that the proposed approach can achieve more competitive single-image super-resolution quality compared with other state-of-the-art methods.

[1]  Cheolkon Jung,et al.  Improving dictionary based image super-resolution with nonlocal total variation regularization , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

[2]  Zhiwei Xiong,et al.  Image hallucination with feature enhancement , 2009, CVPR.

[3]  Xiangjun Zhang,et al.  Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation , 2008, IEEE Transactions on Image Processing.

[4]  M. Ng,et al.  Superresolution image reconstruction using fast inpainting algorithms , 2007 .

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

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

[7]  Leonidas J. Guibas,et al.  Shape google: Geometric words and expressions for invariant shape retrieval , 2011, TOGS.

[8]  Xuelong Li,et al.  A multi-frame image super-resolution method , 2010, Signal Process..

[9]  Shuyuan Yang,et al.  Multitask dictionary learning and sparse representation based single-image super-resolution reconstruction , 2011, Neurocomputing.

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

[11]  Ying Wu,et al.  Super-Resolution Without Dense Flow , 2012, IEEE Transactions on Image Processing.

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

[13]  Truong Q. Nguyen,et al.  Markov Random Field Model-Based Edge-Directed Image Interpolation , 2007, IEEE Transactions on Image Processing.

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

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

[16]  Xuelong Li,et al.  Image Super-Resolution With Sparse Neighbor Embedding , 2012, IEEE Transactions on Image Processing.

[17]  Bing Zeng,et al.  Image super-resolution by curve fitting in the threshold decomposition domain , 2012, J. Vis. Commun. Image Represent..

[18]  Lei Zhang,et al.  An edge-guided image interpolation algorithm via directional filtering and data fusion , 2006, IEEE Transactions on Image Processing.

[19]  Jian Sun,et al.  Generic Image Hallucination with Primal Sketch Prior , 2003, CVPR 2003.

[20]  Weisi Lin,et al.  Geometric Optimum Experimental Design for Collaborative Image Retrieval , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[21]  Yang Tang,et al.  Synchronization of Nonlinear Dynamical Networks With Heterogeneous Impulses , 2014, IEEE Transactions on Circuits and Systems I: Regular Papers.

[22]  Hsieh Hou,et al.  Cubic splines for image interpolation and digital filtering , 1978 .

[23]  Yiguang Chen,et al.  Single-Image Super-Resolution Reconstruction via Learned Geometric Dictionaries and Clustered Sparse Coding , 2012, IEEE Transactions on Image Processing.

[24]  Shuyuan Yang,et al.  Novel Super Resolution Restoration of Remote Sensing Images Based on Compressive Sensing and Example Patches-Aided Dictionary Learning , 2011, 2011 International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping.

[25]  Lin Ma,et al.  Three-tiered network model for image hallucination , 2008, 2008 15th IEEE International Conference on Image Processing.

[26]  Toshihisa Tanaka,et al.  Region-based weighted-norm with adaptive regularization for resolution enhancement , 2011, Digit. Signal Process..

[27]  Lei Zhang,et al.  Centralized sparse representation for image restoration , 2011, 2011 International Conference on Computer Vision.

[28]  Lei Zhang,et al.  Sparse Representation Based Image Interpolation With Nonlocal Autoregressive Modeling , 2013, IEEE Transactions on Image Processing.

[29]  Shuyuan Yang,et al.  Image Noise Reduction via Geometric Multiscale Ridgelet Support Vector Transform and Dictionary Learning , 2013, IEEE Transactions on Image Processing.

[30]  Guillermo Sapiro,et al.  Non-local sparse models for image restoration , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[31]  Peyman Milanfar,et al.  Kernel Regression for Image Processing and Reconstruction , 2007, IEEE Transactions on Image Processing.

[32]  David B. Dunson,et al.  Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images , 2012, IEEE Transactions on Image Processing.

[33]  Chun Chen,et al.  Graph Regularized Sparse Coding for Image Representation , 2011, IEEE Transactions on Image Processing.

[34]  Kin-Man Lam,et al.  Example-based image super-resolution with class-specific predictors , 2009, J. Vis. Commun. Image Represent..

[35]  Aggelos K. Katsaggelos,et al.  Bayesian combination of sparse and non-sparse priors in image super resolution , 2013, Digit. Signal Process..

[36]  Dehui Kong,et al.  Image super-resolution based on multi-space sparse representation , 2010, ICIMCS '10.

[37]  Michael Elad,et al.  Generalizing the Nonlocal-Means to Super-Resolution Reconstruction , 2009, IEEE Transactions on Image Processing.

[38]  Cewu Lu,et al.  Image smoothing via L0 gradient minimization , 2011, ACM Trans. Graph..

[39]  Liang-Tien Chia,et al.  Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Xuelong Li,et al.  Geometry constrained sparse coding for single image super-resolution , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[42]  Junfeng Yang,et al.  A New Alternating Minimization Algorithm for Total Variation Image Reconstruction , 2008, SIAM J. Imaging Sci..

[43]  Xuelong Li,et al.  Image Quality Assessment Based on Multiscale Geometric Analysis , 2009, IEEE Transactions on Image Processing.

[44]  Bin Li,et al.  Semisupervised Dual-Geometric Subspace Projection for Dimensionality Reduction of Hyperspectral Image Data , 2014, IEEE Transactions on Geoscience and Remote Sensing.

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