  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.
