A convergent Plug-and-Play Majorization-Minimization algorithm for Poisson inverse problems
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915889744445440 |
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| author | Modrzyk, Thibaut Etxebeste, Ane Bretin, Élie Maxim, Voichita |
| author_facet | Modrzyk, Thibaut Etxebeste, Ane Bretin, Élie Maxim, Voichita |
| contents | In this paper, we present a novel variational plug-and-play algorithm for Poisson inverse problems. Our approach minimizes an explicit functional which is the sum of a Kullback-Leibler data fidelity term and a regularization term based on a pre-trained neural network. By combining classical likelihood maximization methods with recent advances in gradient-based denoisers, we allow the use of pre-trained Gaussian denoisers without sacrificing convergence guarantees. The algorithm is formulated in the majorization-minimization framework, which guarantees convergence to a stationary point. Numerical experiments confirm state-of-the-art performance in deconvolution and tomography under moderate noise, and demonstrate clear superiority in high-noise conditions, making this method particularly valuable for nuclear medicine applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_24156 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A convergent Plug-and-Play Majorization-Minimization algorithm for Poisson inverse problems Modrzyk, Thibaut Etxebeste, Ane Bretin, Élie Maxim, Voichita Computer Vision and Pattern Recognition In this paper, we present a novel variational plug-and-play algorithm for Poisson inverse problems. Our approach minimizes an explicit functional which is the sum of a Kullback-Leibler data fidelity term and a regularization term based on a pre-trained neural network. By combining classical likelihood maximization methods with recent advances in gradient-based denoisers, we allow the use of pre-trained Gaussian denoisers without sacrificing convergence guarantees. The algorithm is formulated in the majorization-minimization framework, which guarantees convergence to a stationary point. Numerical experiments confirm state-of-the-art performance in deconvolution and tomography under moderate noise, and demonstrate clear superiority in high-noise conditions, making this method particularly valuable for nuclear medicine applications. |
| title | A convergent Plug-and-Play Majorization-Minimization algorithm for Poisson inverse problems |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.24156 |