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arXiv · 2203.00570

Towards a unified view of unsupervised non-local methods for image denoising: the NL-Ridge approach

Sébastien Herbreteau·Charles Kervrann
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Abstract

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.

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Sébastien Herbreteau, Charles Kervrann. 2022-03-01. Towards a unified view of unsupervised non-local methods for image denoising: the NL-Ridge approach. https://doi.org/10.1109/icip46576.2022.9897992

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