EPFL | Biomedical Imaging Group

Multivariate Fields of Experts for Convergent Image Reconstruction

S. Ducotterd, M. Unser

IEEE Transactions on Computational Imaging, vol. 12, pp. 827–838, 2026.

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.

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JOURNAL="{IEEE} Transactions on Computational Imaging",
YEAR="2026",
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