Towards a unified view of unsupervised non-local methods for image denoising: the NL-Ridge approach
This work addresses image denoising for computer vision applications, presenting an incremental improvement by unifying existing methods with a simpler conceptual approach.
The authors tackled the problem of unifying unsupervised non-local methods for image denoising by proposing the NL-Ridge approach, which reconciles patch aggregation methods and outperforms state-of-the-art unsupervised denoisers like BM3D and NL-Bayes in experiments on artificially noisy images.
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.