The Little Engine That Could: Regularization by Denoising (RED)
The Little Engine That Could: Regularization by Denoising (RED)
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DOI：
10.1137/16m1102884
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发表时间：
2017-01-01
影响因子：
2.1
通讯作者：
Milanfar, Peyman
中科院分区：
文献类型：
--
作者：
Romano, Yaniv;Elad, Michael;Milanfar, Peyman
图像去噪是图像处理中一个被广泛研究的问题。事实上，近年来复杂且高效的去噪算法的出现使得一些人认为现有方法在去噪性能方面已接近极限。我们能否利用这一令人瞩目的成果来处理图像处理中的其他任务呢？近期的研究以即插即用先验（P - 3）方法的形式对这一问题给出了肯定的答案，表明任何逆问题都可以通过依次应用图像去噪步骤来解决。这在很大程度上依赖于交替方向乘子法（ADMM）优化技术，以获得这种链式去噪解释。这是图像处理任务利用图像去噪引擎的唯一方式吗？在本文中，我们提供了一个替代的、更强大且更灵活的框架来实现相同的目标。与P - 3方法不同，我们提出了通过去噪进行正则化（RED）的方法：利用去噪引擎来定义逆问题的正则化。我们提出了一个明确的基于图像自适应拉普拉斯算子的正则化函数，使整体目标函数更清晰、定义更明确。由于在选择迭代优化过程以最小化上述函数方面具有完全的灵活性，RED能够结合任何图像去噪算法，能够非常有效地处理一般逆问题，并且保证收敛到全局最优结果。我们对该方法进行了测试，并在图像去模糊和超分辨率问题上展示了最先进的结果。
Removal of noise from an image is an extensively studied problem in image processing. Indeed, the recent advent of sophisticated and highly effective denoising algorithms has led some to believe that existing methods are touching the ceiling in terms of noise removal performance. Can we leverage this impressive achievement to treat other tasks in image processing? Recent work has answered this question positively, in the form of the Plug-and-Play Prior (P-3) method, showing that any inverse problem can be handled by sequentially applying image denoising steps. This relies heavily on the ADMM optimization technique in order to obtain this chained denoising interpretation. Is this the only way in which tasks in image processing can exploit the image denoising engine? In this paper we provide an alternative, more powerful, and more flexible framework for achieving the same goal. As opposed to the P-3 method, we offer Regularization by Denoising (RED): using the denoising engine in de fining the regularization of the inverse problem. We propose an explicit image-adaptive Laplacian-based regularization functional, making the overall objective functional clearer and better defined. With a complete flexibility to choose the iterative optimization procedure for minimizing the above functional, RED is capable of incorporating any image denoising algorithm, can treat general inverse problems very effectively, and is guaranteed to converge to the globally optimal result. We test this approach and demonstrate state-of-the-art results in the image deblurring and super-resolution problems.