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Bayesian Selection for the $\ell _2$ -Potts Model Regularization Parameter: 1-D Piecewise Constant Signal Denoising

Jordan Frecon, Nelly Pustelnik, Nicolas Dobigeon, Herwig Wendt, Patrice Abry
2017 IEEE Transactions on Signal Processing  

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2016 pre-print arXiv:1608.07739v1 fatcat:2ao44bmkuja4zjg3m6a5aq6lzi

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2017 accepted
arXiv:1608.07739v2 fatcat:dlf3nr2ylvfqjhjidyc72qhw4q
Piecewise constant denoising can be solved either by deterministic optimization approaches, based on the Potts model, or by stochastic Bayesian procedures.  ...  Behaviors and performance for the proposed piecewise constant denoising and regularization parameter tuning techniques are studied qualitatively and assessed quantitatively, and shown to compare favorably  ...  Introduction Piecewise constant denoising.  ... 
doi:10.1109/tsp.2017.2715000 fatcat:wisxd74earcqjci47tdyu55er4
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Jordan Frecon, Nelly Pustelnik, Nicolas Dobigeon, Herwig Wendt, Patrice Abry. "Bayesian Selection for the $\ell _2$ -Potts Model Regularization Parameter: 1-D Piecewise Constant Signal Denoising." IEEE Transactions on Signal Processing 65.19 (2017) 5215-5224

Adaptive Filter for Automatic Identification of Multiple Faults in a Noisy OTDR Profile

Jean Pierre von der Weid, Mario H. Souto, Joaquim D. Garcia, Gustavo C. Amaral
2016 Journal of Lightwave Technology  

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2016 pre-print arXiv:1602.04379v1 fatcat:i7jh42vyrnextlpffxcm33rpw4
A two-stage regularization filtering can accurately identify the location of the significant level-shifts with an efficient parameter-free algorithm.  ...  Our case studies compare the new methodology with current available ones and show that it is the most adequate technique for fast detection of multiple unknown level-shifts in a noisy OTDR profile.  ...  Acknowledgment The authors wish to thank brazilian agencies CPNq, CAPES and FAPERJ for financial support.  ... 
doi:10.1109/jlt.2016.2570302 fatcat:narrhl3mcreazjjhk6ohkb3yzi
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Jean Pierre von der Weid, Mario H. Souto, Joaquim D. Garcia, Gustavo C. Amaral. "Adaptive Filter for Automatic Identification of Multiple Faults in a Noisy OTDR Profile." Journal of Lightwave Technology 34.14 (2016) 3418-3424

Regularization, Bayesian Inference, and Machine Learning Methods for Inverse Problems

Ali Mohammad-Djafari
2021 Entropy  

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Classical methods for inverse problems are mainly based on regularization theory, in particular those, that are based on optimization of a criterion with two parts: a data-model matching and a regularization  ...  The Bayesian approach gives more flexibility in choosing these terms and, in particular, the prior term via hierarchical models and hidden variables.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/e23121673 pmid:34945979 pmcid:PMC8699938 fatcat:rwzi5xymtrglbo5lsflr67mpkm
DOAJ
Citation

Ali Mohammad-Djafari. "Regularization, Bayesian Inference, and Machine Learning Methods for Inverse Problems." Entropy 23.12 (2021) 1673

Enhancing joint reconstruction and segmentation with non-convex Bregman iteration

Veronica Corona, Martin Benning, Matthias Joachim Ehrhardt, Lynn Faith Gladden, Richard Mair, Andi Reci, Andrew J Sederman, Stefanie Reichelt, Carola-Bibiane Schönlieb
2019 Inverse Problems  

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Our results for synthetic and real data show that both reconstruction and segmentation are improved compared to the Original content from this work may be used under the terms of the Creative Commons Attribution  ...  A common image processing task for applications that utilise those modalities is image segmentation, typically performed posterior to the reconstruction.  ...  'EP/M00483X/1', EPSRC centre 'EP/N014588/1', the Cantab Capital Institute for the Mathematics of Information, and from CHiPS and NoMADS (Horizon 2020 RISE project grant).  ... 
doi:10.1088/1361-6420/ab0b77 fatcat:5yptypzjvrcqffyzw4fsygcmpq
Szczepanski
Citation

Veronica Corona, Martin Benning, Matthias Joachim Ehrhardt, Lynn Faith Gladden, Richard Mair, Andi Reci, Andrew J Sederman, Stefanie Reichelt, Carola-Bibiane Schönlieb. "Enhancing joint reconstruction and segmentation with non-convex Bregman iteration." Inverse Problems 35.5 (2019)

Modelling the dynamic pattern of surface area in basketball and its effects on team performance

Rodolfo Metulini, Marica Manisera, Paola Zuccolotto
2018 Journal of Quantitative Analysis in Sports (JQAS)  

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Finally, we assess the relation between the regime probabilities and the scored points by means of Vector Auto Regressive (VAR) models.  ...  Specifically, we first employ a Markov Switching Model (MSM) to detect structural changes in the surface area.  ...  The parameters of the model vary across space according to a latent Potts model, modulated by geo-referenced covariates.  ... 
doi:10.1515/jqas-2018-0041 fatcat:b3qwsi7tqjg2vdo7gbjtiorv6m
Citation

Rodolfo Metulini, Marica Manisera, Paola Zuccolotto. "Modelling the dynamic pattern of surface area in basketball and its effects on team performance." Journal of Quantitative Analysis in Sports (JQAS) 14.3 (2018) 117-130

A combined local and global motion estimation and compensation method for cardiac CT

Qiulin Tang, Beshan Chiang, Akinola Akinyemi, Alexander Zamyatin, Bibo Shi, Satoru Nakanishi, Bruce R. Whiting, Christoph Hoeschen
2014 Medical Imaging 2014: Physics of Medical Imaging  

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Single parameter Potts models have often been used to address these multi-region problems, but such parameterization is not sufficient when regions have largely different regularization requirements.  ...  Its L2-regularization ensures proper operation when the feature number exceeds the sample number.  ...  Studies have shown that there is variation in the agreement between operators viewing the same tissue [1] suggesting that a complimentary technique for verification could improve the robustness of the  ... 
doi:10.1117/12.2043492 fatcat:fyzpc5m6jbh7fjohqpdmtzkhte
Citation

Qiulin Tang, Beshan Chiang, Akinola Akinyemi, Alexander Zamyatin, Bibo Shi, Satoru Nakanishi, Bruce R. Whiting, Christoph Hoeschen. "A combined local and global motion estimation and compensation method for cardiac CT." Medical Imaging 2014: Physics of Medical Imaging (2014)

Présentée et soutenue par : INVERSE PROBLEMS IN MEDICAL ULTRASOUND IMAGES-APPLICATIONS TO IMAGE DECONVOLUTION, SEGMENTATION AND SUPER-RESOLUTION Directeur(s) de Thèse : Rapporteurs : Membre(s) du jury

Mme Zhao, M Denis, Kouame Jean, Yves Tourneret, M Ali Mohammad-Djafari, Supelec Jean, M Jean-Christophe, Universite Pesquet, Paris, Président Basarab, Paul Sabatier, Membre Kouame (+5 others)
2016 unpublished

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In a second step, we propose a fast single image super-resolution framework using a new analytical solution to the l2-l2 problems (i.e., 2 -norm regularized quadratic problems), which is applicable for  ...  Due to the intractability of the posterior distribution associated with the proposed Bayesian model, we investigate a Markov chain Monte Carlo (MCMC) technique which generates samples distributed according  ...  Figure 2 . 1 : 21 Hierarchical Bayesian model for the parameter and hyperparameter priors, where the TRF x is modeled by a mixture of GGDs, the hidden label field z follows a Potts MRF and the parameters  ... 
fatcat:abtlh6xttnaunc2ivleex4omcy
Citation

Mme Zhao, M Denis, Kouame Jean, Yves Tourneret, M Ali Mohammad-Djafari, Supelec Jean, M Jean-Christophe, Universite Pesquet, Paris, Président Basarab, Paul Sabatier, Membre Kouame, Paul Sabatier, Membre Jean, Yves Tourneret, Inp Toulouse, Membre. "Présentée et soutenue par : INVERSE PROBLEMS IN MEDICAL ULTRASOUND IMAGES-APPLICATIONS TO IMAGE DECONVOLUTION, SEGMENTATION AND SUPER-RESOLUTION Directeur(s) de Thèse : Rapporteurs : Membre(s) du jury." (2016)

Constrained estimation via the fused lasso and some generalizations [article]

Oscar Hernan Madrid Padilla
2017

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For each row the first column shows the true density along with n = 4000 draws from the respective density. The second column shows the error for the solution given by HTF(k=1).  ...  Middle two panels: results of histogram trend filtering for the f 1 sample (left) and the f 2 sample (right).  ...  Model selection We know turn to the discussion of the parameters D n and τ when p = q = 1.  ... 
doi:10.15781/t2t43jk27 fatcat:icpkcn3lcfaivjrp74xj4z2bqe
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Oscar Hernan Madrid Padilla. "Constrained estimation via the fused lasso and some generalizations." (2017)

Graph Structured Normal Means Inference

James Sharpnack
2018

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signal changes) on the graph are recovered.  ...  We will also show how one can form a decomposition of the graph from a spanning tree which will lead to a test for activity in the graph.  ...  Markov random fields (MRF) provide a succinct framework in which the underlying signal is modeled as a draw from an Ising or Potts model [12, 63] .  ... 
doi:10.1184/r1/6718457.v1 fatcat:t7t3owpj7bbe7j76v7iyfynb6m
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James Sharpnack. "Graph Structured Normal Means Inference." (2018)

Deep Feature Learning for Spectral-Spatial Classification of Hyperspectral Remote Sensing Images

Fahim Irfan Alam, University, My, Jun Zhou
2019

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In order to fully exploit the potential offered by these sensors, there is a need to develop accurate and effective models for spectral-spatial analysis of the recorded data.  ...  This thesis aims at presenting novel strategies for the analysis and classifi cation of hyperspectral remote sensing images, placing the focal point on the investigation on deep networks for the extraction  ...  The classification accuracy is further improved by using L2 regularization and dropout during training.  ... 
doi:10.25904/1912/1943 fatcat:6jewidsvwjcjpf6s5yi77cbofm
Citation

Fahim Irfan Alam, University, My, Jun Zhou. "Deep Feature Learning for Spectral-Spatial Classification of Hyperspectral Remote Sensing Images." (2019)

Factor Graphs for Computer Vision and Image Processing [article]

Lawrence Dzidzai Mutimbu, University, The Australian National, University, The Australian National
2016

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Moreover, they are useful in modelling relationships between variables that are not necessarily probabilistic and allow for efficient marginalisation via a sum-product of probabilities.  ...  Factor graphs have been used extensively in the decoding of error correcting codes such as turbo codes, and in signal processing.  ...  They revealed that minimal differences existed between the various layered models if the right features and parameters were selected.  ... 
doi:10.25911/5d7637697a78e fatcat:do7fnlmjt5edvo76bld6sq3pm4
Citation

Lawrence Dzidzai Mutimbu, University, The Australian National, University, The Australian National. "Factor Graphs for Computer Vision and Image Processing." (2016)