全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

全变差与曲波联合稀疏表示模型与原对偶算法

, PP. 944-950

Keywords: 全变差,曲波变换,稀疏表示,原对偶算法

Full-Text   Cite this paper   Add to My Lib

Abstract:

全变差模型因能有效捕捉图像与视频中的细节信息而被广泛应用于机器视觉中,曲波变换具有较强捕捉二维信号中线状跳变信息的能力。文中结合全变差模型和曲波变换的优点,提出一类能更好地捕捉二维信号特征的联合稀疏表示模型,并用原对偶算法求解该模型,即原对偶全变差曲波算法。实验结果表明,用文中模型及求解算法处理后的图像,其客观质量及主观视觉效果均优于现有算法。文中算法也可用于解决图像去模糊、超分辨率等其它具有挑战性的图像处理问题。

References

[1]  Gonzalez R C, Richard E. Digital Image Processing. New York, USA: Prentice-Hall, 2002
[2]  Rudin L I, Osher S. Total Variation Based Image Restoration with Free Local Constraints // Proc of the IEEE International Conference on Image Processing. Austin, USA, 1994, I: 31-35
[3]  Rudin L I, Osher S, Fatemi E. Nonlinear Total Variation Based Noise Removal Algorithms. Physica D: Nonlinear Phenomena, 1992, 60(1/2/3/4): 259-268
[4]  Candes E J, Demanet L, Donoho D L, et al. Fast Discrete Curvelet Transforms. Multiscale Modeling and Simulation, 2006, 5(3): 861-899
[5]  Donoho D L. Compressed Sensing. IEEE Trans on Information Theory, 2006, 52(4): 1289-1306
[6]  Stephen B, Vandenberghe L. Convex Optimization. Cambridge, UK: Cambridge University Press, 2004
[7]  Vinje W E, Gallant J L. Sparse Coding and Decorrelation in Primary Visual Cortex during Natural Vision. Science, 2000, 287(5456): 1273-1276
[8]  Olshausen B A, Field D J. Sparse Coding of Sensory Inputs. Cu-rrent Opinion in Neurobiology, 2004, 14(4): 481-487
[9]  Aujol J F. Some First-Order Algorithms for Total Variation Based Image Restoration. Journal of Mathematical Imaging and Vision, 2009, 34(3): 307-327
[10]  Scherzer O. Handbook of Mathematical Methods in Imaging. New York, USA: Springer-Verlag, 2010
[11]  Zhang Xiaoqun, Burger M, Osher S. A Unified Primal-Dual Algorithm Framework Based on Bregman Iteration. Journal of Scientific Computing, 2011, 46(1): 20-46
[12]  Setzer S. Split Bregman Algorithm, Douglas-Rachford Splitting and Frame Shrinkage // Proc of the 2nd International Conference on Scale Space and Variational Methods in Computer Vision. Voss, Norway, 2009: 464-476
[13]  Chambolle A, Pock T. A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging. Journal of Mathematical Imaging and Vision, 2011, 40(1): 120-145
[14]  Fadili M J, Starck J L, Murtagh F. Inpainting and Zooming Using Sparse Representations. The Computer Journal, 2009, 52(1): 64-79
[15]  Starck J L, Murtagh F, Fadili M J. Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity. Cambridge, UK: Cambridge University Press, 2010
[16]  Cai Jianfeng, Chan R H, Shen Zuowei. A Framelet-Based Image Inpainting Algorithm. Applied and Computational Harmonic Analysis, 2008, 24(2): 131-149
[17]  Beck A, Teboulle M. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems. SIAM Journal on Imaging Sciences, 2011, 2(1): 183-202
[18]  Bredies K, Lorenz D A. Iterated Hard Shrinkage for Minimization Problems with Sparsity Constraints. SIAM Journal on Scientific Computing, 2008, 30(2): 657-680
[19]  Bredies K, Lorenz D A. Linear Convergence of Iterative Soft-Thresholding. Journal of Fourier Analysis and Applications, 2008, 14(5/6): 813-837
[20]  Blumensath T, Davies M E. Iterative Hard Thresholding for Compressed Sensing. Applied and Computational Harmonic Analysis, 2009, 27(3): 265-274
[21]  Donoho D L, Johnstone J M. Ideal Spatial Adaptation by Wavelet Shrinkage. Biometrika, 1994, 81(3): 425-455

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133