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基于局部结构相似与协同表示的超分辨率图像重建*

, PP. 787-793

Keywords: 局部结构相似性,协同表示,稀疏表示,超分辨率图像,图像重建

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Abstract:

提出一种基于局部几何结构相似性和协同表示的超分辨率图像重建算法.该算法利用l2范数正则化的协同表示和局部几何相似约束模型求解低分辨率图像块在低分辨率字典下的线性表示系数,并利用这一系数重构出高分辨率图像块.文中基于l2范数的系数求解模型可得到解析解而不涉及局部最小解,相较于l1稀疏性约束具有较低的复杂度.实验结果表明,该算法对小尺寸超分辨率图像重建可行且有效,并在重构效果上具有明显的优越性.进一步研究表明,在放大因子增大和存在噪声的情况下,该算法较传统算法重构效果也有显著提高.

References

[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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