BM3D Frames and Variational Image Deblurring
BM3D Frames and Variational Image Deblurring
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DOI：
10.1109/tip.2011.2176954
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发表时间：
2012-04-01
影响因子：
10.6
通讯作者：
Egiazarian, Karen
中科院分区：
文献类型：
--
作者：
Danielyan, Aram;Katkovnik, Vladimir;Egiazarian, Karen
最近在非局部块图像建模的框架内提出了一系列用于各种成像问题的块匹配3-D（BM 3D）算法[1]，[2]。在本文中，我们构建分析和合成框架，形式化BM三维图像建模，并使用这些框架开发新的迭代去模糊算法。我们考虑去模糊问题的两种不同的公式，即，一个由单目标函数的最小化给出，另一个基于两个目标函数的广义纳什均衡（GNE）平衡。后者导致去模糊和去噪操作解耦的算法。证明了算法的收敛性。仿真实验表明，从GNE公式推导出的解耦算法表现出最好的数值和视觉效果，并显示出相对于该领域最先进的技术水平的优越性，证实了BM三维框架作为一种先进的图像建模工具的宝贵潜力。
A family of the block matching 3-D (BM3D) algorithms for various imaging problems has been recently proposed within the framework of nonlocal patchwise image modeling [1], [2]. In this paper, we construct analysis and synthesis frames, formalizing BM3D image modeling, and use these frames to develop novel iterative deblurring algorithms. We consider two different formulations of the deblurring problem, i.e., one given by the minimization of the single-objective function and another based on the generalized Nash equilibrium (GNE) balance of two objective functions. The latter results in the algorithm where deblurring and denoising operations are decoupled. The convergence of the developed algorithms is proved. Simulation experiments show that the decoupled algorithm derived from the GNE formulation demonstrates the best numerical and visual results and shows superiority with respect to the state of the art in the field, confirming a valuable potential of BM3D-frames as an advanced image modeling tool.