Adaptive total variation image deblurring: A majorization-minimization approach

By João Pedro Oliveira, José M. Bioucas-Dias, Mário A. T. Figueiredo. Signal Processing, 2009

406 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1d840981a4623bcd26985300e6aa922266dab7d5

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In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections (2016, neutral): “7 5.2 Image denoising . . . . . . . . . . . . . . 9 5.3 Image super-resolution . . . . . . . . . . 9 5.4 JPEG deblocking . . . . . . . . . . . . . 12 5.5 Image inpainting . . . . . . . . . . . . . 12”
Bayesian computation: a summary of the current state, and samples backwards and forwards (2015, methodology): “Here we use the following hierarchical Bayesian model (Oliveira et al. 2009)”
Bayesian computation: a perspective on the current state, and sampling backwards and forwards (2015, methodology): “Here we use the following hierarchical Bayesian model (Oliveira et al. 2009) where π ( θ | α ) is the (improper) total-variation Markov random ﬁeld, (cid:107)·(cid:107) 1 − 2 denotes the composite (cid:96) 1 − (cid:96) 2 norm and ∇ d is the discrete gradient operator that computes the vertical and…”
Nonlocally Centralized Sparse Representation for Image Restoration (2013, methodology): “In the past decades, extensive studies have been conducted on developing various IR approaches [4-23].”
Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization (2010, methodology): “been later developed to improve the TV models [17-19, 42, 45, 47].”
Proximal Markov chain Monte Carlo algorithms (2013, methodology): “This estimate has been computed with the proximal optimisation algorithm of Afonso et al. (2011), and by using the technique of Oliveira et al. (2009) to determine the value of α .”
Discrete Signal Processing on Graphs: Frequency Analysis (2013, methodology): “l processing, approaches based on the minimization of the total variation for time series (13) are called regulatization. They have been used for signal denoising, deblurring and recovery [26], [27], [28], [29]. In data classiﬁcation, approaches based on solving minimization problems similar to (31)”