mlsanityData to 14 Oct 2026

Multivariate Fields of Experts for Convergent Image Reconstruction

arXiv 2508.06490 ↗First posted 8 Aug 2025eess.IV cs.CV cs.LGcited by 2

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Abstract (arXiv)

We introduce the multivariate fields of experts, a new framework for the learning of image priors. Our model generalizes existing fields of experts methods by incorporating multivariate potential functions constructed via Moreau envelopes of the ℓ_∞-norm. We demonstrate the effectiveness of our proposal across a range of inverse problems that include image denoising, deblurring, compressed-sensing magnetic-resonance imaging, and computed tomography. The proposed approach outperforms comparable univariate models and achieves performance close to that of deep-learning-based regularizers while being significantly faster, requiring fewer parameters, and being trained on substantially fewer data. In addition, our model retains a high level of interpretability due to its structured design. It is supported by theoretical convergence guarantees which ensure reliability in sensitive reconstruction tasks.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 23 of its 51 references that are in the map. First posted on arXiv 8 Aug 2025.

Builds on

23 of 51 references.

References · 23

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11 more
  1. 2014iPiano: Inertial Proximal Algorithm for Non-Convex Optimizationcited by 30
  2. 2023What's in a Prior? Learned Proximal Networks for Inverse Problemscited by 25
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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Nonasymptotic Convergence Rates for Plug-and-Play Methods With MMSE Denoiserscited by 2
  2. 2025Breaking the Weak Recovery Limit in Random Phase Retrieval with Learned Regularizerscited by 0