论文标题
迈向无监督的非本地方法的统一视图：NL-Ridge方法
Towards a unified view of unsupervised non-local methods for image denoising: the NL-Ridge approach
论文作者
论文摘要
我们提出了一种无监督的非本地方法的统一视图，以使噪声图像贴片线性结合。在不同的建模和估计框架中建立的最佳方法是两步算法。利用Stein的无偏风险估计（确定）为第一步和“内部适应”（从深度学习理论中借来的概念），我们证明了我们的NL-Ridge方法使我们能够调和几种用于图像DeNoising的补丁聚合方法。在第二步中，我们的闭合聚合权重是通过多元脊回归计算的。人为嘈杂的图像进行的实验表明，NL-Ridge可能会胜过确立的最先进的无监督不可监督者，例如BM3D和NL-Bayes，以及最近的无监督的深度学习方法，同时概念上更简单。
We propose a unified view of unsupervised non-local methods for image denoising that linearily combine noisy image patches. The best methods, established in different modeling and estimation frameworks, are two-step algorithms. Leveraging Stein's unbiased risk estimate (SURE) for the first step and the "internal adaptation", a concept borrowed from deep learning theory, for the second one, we show that our NL-Ridge approach enables to reconcile several patch aggregation methods for image denoising. In the second step, our closed-form aggregation weights are computed through multivariate Ridge regressions. Experiments on artificially noisy images demonstrate that NL-Ridge may outperform well established state-of-the-art unsupervised denoisers such as BM3D and NL-Bayes, as well as recent unsupervised deep learning methods, while being simpler conceptually.
