Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution
In this paper, we introduce a variational Bayesian algorithm (VBA) for image blind deconvolution. Our VBA generic framework incorporates smoothness priors on the unknown blur/image and possible affine constraints (e.g., sum to one) on the blur kernel, integrating the VBA within a neural network paradigm following an unrolling methodology. The proposed architecture is trained in a supervised fashion, which allows us to optimally set two key hyperparameters of the VBA model and leads to further improvements in terms of resulting visual quality. Various experiments involving grayscale/color images and diverse kernel shapes, are performed. The numerical examples illustrate the high performance of our approach when compared to state-of-the-art techniques based on optimization, Bayesian estimation, or deep learning.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:PP |
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| Enthalten in: |
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society - PP(2022) vom: 20. Dez. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Huang, Yunshi [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 04.04.2023 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1109/TIP.2022.3224322 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM35520181X |
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| 520 | |a In this paper, we introduce a variational Bayesian algorithm (VBA) for image blind deconvolution. Our VBA generic framework incorporates smoothness priors on the unknown blur/image and possible affine constraints (e.g., sum to one) on the blur kernel, integrating the VBA within a neural network paradigm following an unrolling methodology. The proposed architecture is trained in a supervised fashion, which allows us to optimally set two key hyperparameters of the VBA model and leads to further improvements in terms of resulting visual quality. Various experiments involving grayscale/color images and diverse kernel shapes, are performed. The numerical examples illustrate the high performance of our approach when compared to state-of-the-art techniques based on optimization, Bayesian estimation, or deep learning | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Pesquet, Jean-Christophe |e verfasserin |4 aut | |
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