10,721 research outputs found
Accelerated graph-based nonlinear denoising filters
Denoising filters, such as bilateral, guided, and total variation filters,
applied to images on general graphs may require repeated application if noise
is not small enough. We formulate two acceleration techniques of the resulted
iterations: conjugate gradient method and Nesterov's acceleration. We
numerically show efficiency of the accelerated nonlinear filters for image
denoising and demonstrate 2-12 times speed-up, i.e., the acceleration
techniques reduce the number of iterations required to reach a given peak
signal-to-noise ratio (PSNR) by the above indicated factor of 2-12.Comment: 10 pages, 6 figures, to appear in Procedia Computer Science, vol.80,
2016, International Conference on Computational Science, San Diego, CA, USA,
June 6-8, 201
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
Plug-and-play (PnP) is a non-convex framework that integrates modern
denoising priors, such as BM3D or deep learning-based denoisers, into ADMM or
other proximal algorithms. An advantage of PnP is that one can use pre-trained
denoisers when there is not sufficient data for end-to-end training. Although
PnP has been recently studied extensively with great empirical success,
theoretical analysis addressing even the most basic question of convergence has
been insufficient. In this paper, we theoretically establish convergence of
PnP-FBS and PnP-ADMM, without using diminishing stepsizes, under a certain
Lipschitz condition on the denoisers. We then propose real spectral
normalization, a technique for training deep learning-based denoisers to
satisfy the proposed Lipschitz condition. Finally, we present experimental
results validating the theory.Comment: Published in the International Conference on Machine Learning, 201
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