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MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model
Conference proceeding

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model

Jyothi Rikhab Chand and Mathews Jacob
Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998)
2025
DOI: 10.1109/ICASSP49660.2025.10889960
url
https://pmc.ncbi.nlm.nih.gov/articles/PMC12974777/View
Open Access

Abstract

We propose a multi-scale deep energy model that is strongly convex in the local neighbourhood around the data manifold to represent its probability density, with application in inverse problems. In particular, we represent the negative log-prior as a multi-scale energy model parameterized by a Convolutional Neural Network (CNN). We restrict the gradient of the CNN to be locally monotone, which constrains the model as a Locally Convex Multi-Scale Energy (LC-MuSE). We use the learned energy model in image-based inverse problems, where the formulation offers several desirable properties: i) uniqueness of the solution, ii) convergence guarantees to a minimum of the inverse problem, and iii) robustness to input perturbations. In the context of parallel Magnetic Resonance (MR) image reconstruction, we show that the proposed method performs better than the state-of-the-art convex regularizers, while the performance is comparable to plug-and-play regularizers and end-to-end trained methods.
 Energy model Locally convex regularizer Parallel MR image reconstruction

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