Abstract：An approach for super-resolution image reconstruction is presented based on local structural similarity and collaborative representation. The collaborative representation l2-norm regularization and local similarity constraint are employed to seek a linear combination for a patch of low-resolution input image with respect to the low-resolution dictionary. Then, the high-resolution image patch is generated by virtue of the coefficients of this combination and the high-resolution dictionary. In addition, the l2-norm based objective function implies an analytical solution and it does not involve local minima. Hence, it performs at a lower complexity compared to l1-sparsity constraint model. The experimental results demonstrate that the proposed method is feasible and effective for small super-resolution image reconstruction and outperforms the bicubic interpolation method and sparse representation super-resolution model on both visual effect and numerical results. Further research shows that the proposed method also performs well for large magnification factors and noisy data.
蔡苗苗，谈元鹏，曹飞龙. 基于局部结构相似与协同表示的超分辨率图像重建*[J]. 模式识别与人工智能, 2014, 27(9): 787-793.
CAI Miao-Miao, TAN Yuan-Peng, CAO Fei-Long. Super-Resolution Image Reconstruction Based on Local Structural Similarity and Collaborative Representation. , 2014, 27(9): 787-793.
[1] Biswas S, Aggarwal G, Flynn P J. Pose-Robust Recognition of Low-resolution Face Images // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 2011: 601-608 [2] Hou H S, Andrews H. Cubic Splines for Image Interpolation and Digital Filtering. IEEE Trans on Acoustics, Speech and Signal Processing, 1978, 26(6): 508-517 [3] Allebach J, Wong P W. Edge-Directed Interpolation // Proc of the International Conference on Image Processing. Lausanne, Switzerland, 1996, III: 707-710 [4] Freeman W T, Jones T R, Pasztor E C. Example-Based Super-Resolution. IEEE Computer Graphics and Applications, 2002, 22(2): 56-65 [5] Chang H, Yeung D Y, Xiong Y M. Super-Resolution through Neighbor Embedding // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA , 2004, I: 275-282 [6] Roweis S T, Saul L K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 2000, 290(5500): 2323-2326 [7] Yang J C, Wright J, Huang T S, et al. Image Super-Resolution via Sparse Representation. IEEE Trans on Image Processing, 2010, 19(11): 2861-2873 [8] Wright J, Yang A Y, Ganesh A, et al. Robust Face Recognition via Sparse Representation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227 [9] Rigamonti R, Brown M A, Lepetit V. Are Sparse Representations Really Relevant for Image Classification? // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 2011: 1545-1552 [10] Shi Q F, Eriksson A, van den Hengel A, et al. Is Face Recognition Really a Compressive Sensing Problem? // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 2011: 553-560 [11] Zhang D, Yang M, Feng X C. Sparse Representation or Collaborative Representation: Which Helps Face Recognition? // Proc of the IEEE International Conference on Computer Vision. Barcelona, Spain, 2011: 471-478 [12] Zhu P F, Zhang L, Hu Q H, et al. Multi-scale Patch Based Collaborative Representation for Face Recognition with Margin Distribution Optimization // Proc of the 12th European Conference on Computer Vision. Firenze, Italy, 2012: 822-835 [13] Zhang X J, Wu X L. Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation. IEEE Trans on Image Processing, 2008, 17(6): 887-896 [14] Li X, Orchard M T. New Edge-Directed Interpolation. IEEE Trans on Image Processing, 2001, 10(10): 1521-1527 [15] Lee H, Battle A, Raina R, et al. Efficient Sparse Coding Algorithms // Proc of the 20th Annual Conference on Neural Information Processing Systems. Vancouver, Canada, 2006: 801-808 [16] Peng X, Zhang L, Zhang Y, et al. Learning Locality-Constrained Collaborative Representation for Face Recognition. Pattern Reconition, 2014, 47(9): 2794-2806 [17] Yang M, Zhang L, Zhang D, et al. Relaxed Collaborative Representation for Pattern Classification // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA, 2012: 2224-2231 [18] Wang Z, Bovik A C, Sheikh H R, et al. Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Trans on Image Processing, 2004, 13(4): 600-612
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