期刊文献+

基于差分算子边缘约束先验的图像盲复原算法

Image restoration algorithm based on edge constraint prior of differential operator
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摘要 为了有效克服图像在复原过程的边缘退化、振铃效应等影响,使空间域的边缘先验信息能够灵活的添加到图像复原算法中,依据差分算子描述检测边缘的特性,将数字摄影测量学中的拉普拉斯算子、Robert梯度算子、方向差分算子作为一种新的边缘约束先验引入到图像复原过程中,同时使用Toeplitz矩阵实现图像在空间域解卷积的过程,提出了一种以差分算子为边缘约束先验的空域图像复原算法。模拟数据的实验结果体现了更多的细节信息,相关评价指标表明了该算法的有效性。 In order to overcome the edge degradation of the image in the recovery processing and add the edge prior information of the space domain into the image restoration algorithms,Laplacian operator,Robert operator and direct differential operator of digital photogrammetry is introduced as an edge constraint prior into the process of image restoration,analysis and examine the process to realize the deconvolution of image in the space domain using Toeplitz matrix and promoted a space domain image restoration algorithm using differen-tial operator as edge constraint prior.The experimental results of the simulated data demonstrate more detailed information and proved the validity of the algorithm.
出处 《计算机工程与设计》 CSCD 北大核心 2011年第12期4107-4110,共4页 Computer Engineering and Design
基金 国家863高技术研究发展计划基金项目(2006AA12Z110) 国家自然科学基金项目(60778051)
关键词 图像复原 边缘保持 解卷积 差分算子 先验约束 image restoration edge-preserving deconvolution differential operator constraint prior
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参考文献14

  • 1Gerace I,Panolfl R,Pucci EA new estimation of blur in the blind restoration problem[C].International Conference on Image Pro- cessing,2003:261-264.
  • 2Charles L Matson,Kathy Borelli,Stuart Jefferies.Fast and opti- mal multiframe blind deconvolution algorithm for high-resolu- tion ground-based imaging of space objects[J].Applied Optics, 2009,48( 1 ):A75-A92.
  • 3沈峘,李舜酩,毛建国,辛江慧.数字图像复原技术综述[J].中国图象图形学报,2009,14(9):1764-1775. 被引量:44
  • 4解凯.超分辨率图像复原技术综述[J].北京印刷学院学报,2007,15(6):41-44. 被引量:3
  • 5Levin A,Weiss Y.User assisted separation of reflections from a single image using a sparsity prior[J].PAMl,2007,29(9): 1647- 1654.
  • 6Green J J,Hunt B R.lmproved restoration of space object ima- gery[J].Journal of the Optical Society of America A,Optics, Image Science,and Vision, 1999,16( 12):2859-2865.
  • 7Christou J C,Roorda A,Williams D R.Deconvolution of adaptive optics retinal images[J].Joumal of the Optical Society of America A,Optics,lmage Science,and V s on,2004,21 (8): 1393-1401.
  • 8田雨,饶长辉,魏凯.基于帧选择和多帧降质图像盲解卷积的自适应光学图像恢复[J].天文学报,2008,49(4):455-462. 被引量:8
  • 9Brette S,dier J.Optimized single site update algorithms for image deblurring[C].Proceeding of the International Conference on Image Processing,2006:65-68.
  • 10Krishnan D,Fergus R.Fast image deconvolution using hyper-lap- lacian priors supplementary material[J].Neural Information Pro- cessing Systems Conference,2009.

二级参考文献130

  • 1Banham M R, Katsaggelos A K. Digital image restoration [ J]. Signal Processing, 1997, 14(2) : 24-41.
  • 2Robbins G M, Huang T S. Inverse filtering for linear shift-variant imaging systems [ J ]. Proceedings of IEEE, 1972, 60 (7) : 862-872.
  • 3Brigham E O, Smith H W, Bostick F X, et al. An iterative technique for determining inverse filters [ J]. IEEE Transactions on Geoscience Electronics, 1968, 6(2) :86-96.
  • 4Springer T, Torres J, Pearce J A, et al. Restoration of thermographic images using iterative inverse filtering [ J]. Engineering in Medicine and Biology Society, 1989, 2 (2) : 365-366.
  • 5Chottera A, Jullien G. Recursive digital filters in image processing [ A ]. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing [C], Tulsa, OK, USA, 1978: 757-760.
  • 6Hsiao C C, Chi C Y. Image modeling and restoration by higher-order statistics based inverse filters [ A ]. In: Proceedings of IEEE Seventh SP Workshop on Statistical Signal and Array Processing [ C ], Quebec, Canada, 1994, 203-206.
  • 7Chi C Y, Wu M C. A unified class of inverse filter criteria using two cumulants for blind deconvolution and equalization [ A ]. In: Conference on ICASSP- 95 [ C ] , Detroit, MI, USA , 1995, 3: 1960-1963.
  • 8Galatsanos N P,Katsaggelos A K. Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation [J]. IEEE Transactions on Image Processing, 1992, 1(3) :322-336.
  • 9Nakano K, Eguehi M, Toyota Y, et al. On regularization for image restoration problems from the viewpoint of a bayesian information criterion [ A ]. In: Proceedings of International Conference on IECON [C], Maui,HI, USA, 1993: 2257-2261.
  • 10Reeves S J. Optimal regularized image restoration with constraints [ A]. In: Proceedings of IEEE International Conference on ICASSP [ C] , San Francisco, CA, USA, 1992, 3:301-304.

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