Image Restoration with Multiple Hard Constraints on Data-Fidelity to Blurred/Noisy Image Pair
Image Restoration with Multiple Hard Constraints on Data-Fidelity to Blurred/Noisy Image Pair
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DOI：
10.1587/transinf.2016pcp0003
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发表时间：
2017-09
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影响因子：
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通讯作者：
Saori Takeyama;Shunsuke Ono;I. Kumazawa
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文献类型：
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作者：
Saori Takeyama;Shunsuke Ono;I. Kumazawa
现有的模糊/噪声图像对的图像去模糊方法采用两步方法：模糊核估计和图像恢复。与单图像去模糊方法相比，它们可以获得更好和更稳定的模糊核估计。另一方面，在图像恢复步骤中，它们不利用关于噪声图像的信息，或者它们需要对相互依赖的参数进行特别调整。本文重点研究了图像复原步骤，提出了一种新的利用模糊/噪声图像对的复原方法。在我们的方法中，图像恢复问题被制定为一个约束凸优化问题，其中的数据保真度的模糊图像和噪声图像被适当地考虑作为多个硬约束。这提供了（i）当模糊图像还包含噪声时的高质量恢复;（ii）对模糊核的估计误差的鲁棒性;以及（iii）容易的参数设置。我们还提供了一个有效的算法来解决我们的优化问题的基础上，所谓的交替方向的乘法器（ADMM）。实验结果支持我们的主张。关键词：ADMM，去模糊，硬约束，图像恢复，约束凸优化
Existing image deblurring methods with a blurred/noisy image pair take a two-step approach: blur kernel estimation and image restoration. They can achieve better and much more stable blur kernel estimation than single image deblurring methods. On the other hand, in the image restoration step, they do not exploit the information on the noisy image, or they require ad hoc tuning of interdependent parameters. This paper focuses on the image restoration step and proposes a new restoration method of using a blurred/noisy image pair. In our method, the image restoration problem is formulated as a constrained convex optimization problem, where data-fidelity to a blurred image and that to a noisy image is properly taken into account as multiple hard constraints. This offers (i) high quality restoration when the blurred image also contains noise; (ii) robustness to the estimation error of the blur kernel; and (iii) easy parameter setting. We also provide an efficient algorithm for solving our optimization problem based on the so-called alternating direction method of multipliers (ADMM). Experimental results support our claims. key words: ADMM, deblurring, hard constraints, image restoration, constrained convex optimization