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基于变分的盲图像复原质量评价指标

成孝刚 安明伟 阮雅端 陈启美

成孝刚, 安明伟, 阮雅端, 陈启美. 基于变分的盲图像复原质量评价指标. 自动化学报, 2013, 39(4): 418-423. doi: 10.3724/SP.J.1004.2013.00418
引用本文: 成孝刚, 安明伟, 阮雅端, 陈启美. 基于变分的盲图像复原质量评价指标. 自动化学报, 2013, 39(4): 418-423. doi: 10.3724/SP.J.1004.2013.00418
CHENG Xiao-Gang, AN Ming-Wei, RUAN Ya-Duan, CHEN Qi-Mei. A Modern Image Quality Measurement Method for Blind Image Restoration. ACTA AUTOMATICA SINICA, 2013, 39(4): 418-423. doi: 10.3724/SP.J.1004.2013.00418
Citation: CHENG Xiao-Gang, AN Ming-Wei, RUAN Ya-Duan, CHEN Qi-Mei. A Modern Image Quality Measurement Method for Blind Image Restoration. ACTA AUTOMATICA SINICA, 2013, 39(4): 418-423. doi: 10.3724/SP.J.1004.2013.00418

基于变分的盲图像复原质量评价指标

doi: 10.3724/SP.J.1004.2013.00418
详细信息
    通讯作者:

    成孝刚

A Modern Image Quality Measurement Method for Blind Image Restoration

  • 摘要: 盲图像复原过程中,图像质量评价至关重要. 通过分析重构图像质量与其总变分值之间的关系, 提出了用于图像复原的一种基于总变分(Total bounded variation, TBV)的图像质量评估方法, 并构建关系模型, 证明了原始清晰图像的总变分值在所有模糊图像中具有极大值, 且在所有重构图像的变分值中具有极小值. 通过分析, 得出结论: 当总变分取极值时, 基于所提度量方法, 可以获得更好的盲图像重构效果. 最后, 比较了原始清晰图像、模糊图像和重构图像之间的变分值, 计算机仿真验证了该方法的有效性和准确性.
  • [1] Liu X W, Huang L H. Split Bregman iteration algorithm for total bounded variation regularization based image deblurring. Journal of Mathematical Analysis and Applications, 2010, 372(2): 486-495[2] Lieu L H, Vese L A. Image restoration and decomposition via bounded total variation and negative Hilbert-Sobolev spaces. Applied Mathematics and Optimization, 2008, 58(2): 167-193[3] Kirova Y M, Pena P C, Hijal T, Fournier-Bidoz N, Laki F, Sigal-Zafrani B. Improving the definition of tumor bed boost with the use of surgical clips and image registration in breast cancer patients. International Journal of Radiation Oncology Biology Physics, 2010, 78(5): 1352-1355[4] Rudin L I, Osher S, Fatemi E. Nonlinear total vriation based noise removal algorithms. Physica D, 1992, 60(4): 259-268[5] Blomgren P V. Total variation method for restoration of vector valued images [Ph.D. dissertation], University of California, USA, 1998[6] Maleki M, Latifi M, Amani-Tehran M. Definition of structural features of nano coated webs by image processing methods. International Journal of Nanotechnology, 2009, 6(12): 1131-1154[7] Quarello E, Trabbia A. High-definition flow combined with spatiotemporal image correlation in the diagnosis of fetal coarctation of the aorta. Ultrasound in Obstetrics and Gynecology, 2009, 33(3): 365-367[8] Cheng X G, An M W, Chen Q M. Image distortion metric based on total bounded variation. China Communications, 2012, 9(2): 79-85[9] Cheng Xiao-Gang, Chen Qi-Mei, Liu Guo-Qing. The relation between total bounded variation and image definition detection. Journal of Beijing University of Posts and Telecommunications, 2009, 32(S1): 120-122, 139(成孝刚, 陈启美, 刘国庆. 总有界变差与图像清晰度之间的关系. 北京邮电大学学报, 2007, 32(S1): 120-122, 139)[10] Awwal A A S, Rice K, Taha T. Fast implementation of matched-filter-based automatic alignment image processing. Optics and Laser Technology, 2009, 41(2): 193-197[11] Saad M A, Bovik A C, Charrier C. A DCT statistics-based blind image quality index. IEEE Signal Processing Letters, 2010, 17(6): 583-586[12] Li Bo, Su Zhi-Xun, Liu Xiu-Ping. An adaptive PDE image processing method based on Lp norm. Acta Automatica Sinica, 2008, 34(8): 849-853(李波, 苏志勋, 刘秀平. 基于Lp范数的局部自适应偏微分方程图像恢复. 自动化学报, 2008, 34(8): 849-853)[13] Moorthy A, Bovik A. Blind image quality assessment: from natural scene statistics to perceptual quality. IEEE Transactions on Image Processing, 2011, 20(12): 3350-3364
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出版历程
  • 收稿日期:  2011-02-28
  • 修回日期:  2011-06-22
  • 刊出日期:  2013-04-20

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