Probabilistic Modeling and Inference for Sequential Space-Varying Blur Identification
| Autor: | Victor Elvira, Yunshi Huang, Emilie Chouzenoux |
|---|---|
| Přispěvatelé: | Laboratoire d'Informatique Gaspard-Monge (LIGM), École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel, OPtimisation Imagerie et Santé (OPIS), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-CentraleSupélec-Université Paris-Saclay, School of Mathematics - University of Edinburgh, University of Edinburgh, ANR-17-CE40-0004,MajIC,Algorithmes de Majoration-Minimisation pour le traitement d'images(2017), ANR-17-CE40-0031,PISCES,Méthodes d'échantillonnage d'importance adaptatives pour l'inférence Bayésienne dans les systèmes complexes(2017) |
| Rok vydání: | 2021 |
| Předmět: |
Blur identification
Computer science Posterior probability ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Probabilistic logic 020206 networking & telecommunications 02 engineering and technology Bayesian estimation Computer Science Applications spatially-variant blur Computational Mathematics Kernel (image processing) Signal Processing Parametric model 0202 electrical engineering electronic engineering information engineering particle filtering 020201 artificial intelligence & image processing Uncertainty quantification Particle filter [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing Algorithm Image restoration Parametric statistics |
| Zdroj: | IEEE Transactions on Computational Imaging IEEE Transactions on Computational Imaging, IEEE, 2021, 7, pp.531-546 Huang, Y, Chouzenoux, E & Elvira, V 2021, ' Probabilistic modeling and inference for sequential space-varying blur identification ', IEEE Transactions on Computational Imaging, vol. 7, pp. 531-546 . https://doi.org/10.1109/TCI.2021.3081059 IEEE Transactions on Computational Imaging, 2021, 7, pp.531-546. ⟨10.1109/TCI.2021.3081059⟩ |
| ISSN: | 2334-0118 2573-0436 2333-9403 |
| Popis: | International audience; The identification of parameters of spatially variant blurs given a clean image and its blurry noisy version is a challenging inverse problem of interest in many application fields, such as biological microscopy and astronomical imaging. In this paper, we consider a parametric model of the blur and introduce an 1D state-space model to describe the statistical dependence among the neighboring kernels. We apply a Bayesian approach to estimate the posterior distribution of the kernel parameters given the available data. Since this posterior is intractable for most realistic models, we propose to approximate it through a sequential Monte Carlo approach by processing all data in a sequential and efficient manner. Additionally, we propose a new sampling method to alleviate the particle degeneracy problem, which is present in approximate Bayesian filtering, particularly in challenging concentrated posterior distributions. The considered method allows us to process sequentially image patches at a reasonable computational and memory costs. Moreover, the probabilistic approach we adopt in this paper provides uncertainty quantification which is useful for image restoration. The practical experimental results illustrate the improved estimation performance of our novel approach, demonstrating also the benefits of exploiting the spatial structure the parametric blurs in the considered models. |
| Databáze: | OpenAIRE |
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