Search Results for author: Mario A. T. Figueiredo

Found 11 papers, 1 papers with code

External Patch-Based Image Restoration Using Importance Sampling

no code implementations9 Jul 2018 Milad Niknejad, Jose M. Bioucas-Dias, Mario A. T. Figueiredo

This paper introduces a new approach to patch-based image restoration based on external datasets and importance sampling.

Image Restoration

Image Restoration Using Conditional Random Fields and Scale Mixtures of Gaussians

no code implementations9 Jul 2018 Milad Niknejad, Jose M. Bioucas-Dias, Mario A. T. Figueiredo

This paper proposes a general framework for internal patch-based image restoration based on Conditional Random Fields (CRF).

Image Denoising Image Interpolation +1

Impulsive Noise Robust Sparse Recovery via Continuous Mixed Norm

1 code implementation12 Apr 2018 Amirhossein Javaheri, Hadi Zayyani, Mario A. T. Figueiredo, Farrokh Marvasti

In this paper, we exploit a Continuous Mixed Norm (CMN) for robust sparse recovery instead of $\ell_p$-norm.

Class-specific image denoising using importance sampling

no code implementations21 Jun 2017 Milad Niknejad, Jose M. Bioucas-Dias, Mario A. T. Figueiredo

In this paper, we propose a new image denoising method, tailored to specific classes of images, assuming that a dataset of clean images of the same class is available.

Image Denoising

Class-specific Poisson denoising by patch-based importance sampling

no code implementations9 Jun 2017 Milad Niknejad, Jose M. Bioucas-Dias, Mario A. T. Figueiredo

In this paper, we address the problem of recovering images degraded by Poisson noise, where the image is known to belong to a specific class.

Denoising

Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation

no code implementations CVPR 2017 Zheng Xu, Mario A. T. Figueiredo, Xiaoming Yuan, Christoph Studer, Tom Goldstein

Relaxed ADMM is a generalization of ADMM that often achieves better performance, but its efficiency depends strongly on algorithm parameters that must be chosen by an expert user.

Synthesis versus analysis in patch-based image priors

no code implementations20 Feb 2017 Mario A. T. Figueiredo

This paper shows that there is another analysis vs synthesis dichotomy, in terms of how the whole image is related to the patches, and that all existing patch-based formulations that provide a global image prior belong to the analysis category.

Image Denoising

Adaptive ADMM with Spectral Penalty Parameter Selection

no code implementations24 May 2016 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 objective functions.

Sparse Estimation with Strongly Correlated Variables using Ordered Weighted L1 Regularization

no code implementations14 Sep 2014 Mario A. T. Figueiredo, Robert D. Nowak

This paper studies ordered weighted L1 (OWL) norm regularization for sparse estimation problems with strongly correlated variables.

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