LAMA-Net: A Convergent Network Architecture for Dual-Domain Reconstruction

• URL: http://arxiv.org/abs/2507.22316v1
• Date: Wed, 30 Jul 2025 01:16:38 GMT
• Title: LAMA-Net: A Convergent Network Architecture for Dual-Domain Reconstruction
• Authors: Chi Ding, Qingchao Zhang, Ge Wang, Xiaojing Ye, Yunmei Chen,
• Abstract summary: We propose a learnable variation model that learns to leverage information from both image and measurement domains for image reconstruction.<n>We show that LAMA directly interprets a highly interpretable neural network architecture called LAMA-Net.
• Score: 4.573246328161056
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a learnable variational model that learns the features and leverages complementary information from both image and measurement domains for image reconstruction. In particular, we introduce a learned alternating minimization algorithm (LAMA) from our prior work, which tackles two-block nonconvex and nonsmooth optimization problems by incorporating a residual learning architecture in a proximal alternating framework. In this work, our goal is to provide a complete and rigorous convergence proof of LAMA and show that all accumulation points of a specified subsequence of LAMA must be Clarke stationary points of the problem. LAMA directly yields a highly interpretable neural network architecture called LAMA-Net. Notably, in addition to the results shown in our prior work, we demonstrate that the convergence property of LAMA yields outstanding stability and robustness of LAMA-Net in this work. We also show that the performance of LAMA-Net can be further improved by integrating a properly designed network that generates suitable initials, which we call iLAMA-Net. To evaluate LAMA-Net/iLAMA-Net, we conduct several experiments and compare them with several state-of-the-art methods on popular benchmark datasets for Sparse-View Computed Tomography.

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