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An Empirical Study of ADMM for Nonconvex Problems
Zheng Xu, Soham De, Mario Figueiredo +2
The alternating direction method of multipliers (ADMM) is a common optimization tool for solving constrained and non-differentiable problems. We provide an empirical study of the p…
Restoring STM images via Sparse Coding: noise and artifact removal
João P. Oliveira, Ana Bragança, José Bioucas-Dias +4
In this article, we present a denoising algorithm to improve the interpretation and quality of scanning tunneling microscopy (STM) images. Given the high level of self-similarity o…
Adaptive ADMM with Spectral Penalty Parameter Selection
Zheng Xu, Mario A. T. Figueiredo, Tom Goldstein
The alternating direction method of multipliers (ADMM) is a versatile tool for solving a wide range of constrained optimization problems, with differentiable or non-differentiable…
Image Restoration with Locally Selected Class-Adapted Models
Afonso M. Teodoro, José M. Bioucas-Dias, Mário A. T. Figueiredo
State-of-the-art algorithms for imaging inverse problems (namely deblurring and reconstruction) are typically iterative, involving a denoising operation as one of its steps. Using…
Image Restoration and Reconstruction using Variable Splitting and Class-adapted Image Priors
Afonso M. Teodoro, José M. Bioucas-Dias, Mário A. T. Figueiredo
This paper proposes using a Gaussian mixture model as a prior, for solving two image inverse problems, namely image deblurring and compressive imaging. We capitalize on the fact th…