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Semi-blind image restoration via Mumford-Shah regularization

机译:通过Mumford-Shah正则化进行半盲图像恢复

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Image restoration and segmentation are both classical problems, that are known to be difficult and have attracted major research efforts. This paper shows that the two problems are tightly coupled and can be successfully solved together. Mutual support of image restoration and segmentation processes within a joint variational framework is theoretically motivated, and validated by successful experimental results. The proposed variational method integrates semi-blind image deconvolution (parametric blur-kernel), and Mumford-Shah segmentation. The functional is formulated using the /spl Gamma/-convergence approximation and is iteratively optimized via the alternate minimization method. While the major novelty of this work is in the unified treatment of the semi-blind restoration and segmentation problems, the important special case of known blur is also considered and promising results are obtained.
机译:图像恢复和分割都是经典问题,众所周知这很困难并且吸引了主要的研究工作。本文表明,这两个问题紧密相连,可以一起成功解决。从理论上讲,在联合变量框架内相互支持图像恢复和分割过程,并通过成功的实验结果进行了验证。提出的变分方法集成了半盲图像反卷积(参数模糊核)和Mumford-Shah分割。该函数使用/ spl Gamma /收敛近似公式表示,并通过备用最小化方法进行迭代优化。虽然这项工作的主要新颖之处在于对半盲恢复和分割问题的统一处理,但同时也考虑了已知模糊的重要特殊情况,并获得了可喜的结果。

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