Transferable Optimization Network for Cross-Domain Image Reconstruction

2026-03-08

AI Summary

This work addresses the challenge of limited training data in image reconstruction tasks, particularly in cross-domain scenarios such as undersampled MRI, where performance is often constrained. The authors propose a two-stage transfer learning framework: first, a universal feature extractor is learned via bilevel optimization from heterogeneous multi-source data—including diverse anatomical structures, sampling rates, and natural images—and subsequently, a lightweight adapter is trained for the target domain. By uniquely integrating bilevel optimization with cross-domain transfer, this approach establishes a transferable regularization mechanism for reconstruction. It achieves significantly improved image quality under few-shot conditions and demonstrates strong generalization across varying anatomical structures, imaging modalities, and sampling rates.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

We develop a novel transfer learning framework to tackle the challenge of limited training data in image reconstruction problems. The proposed framework consists of two training steps, both of which are formed as bi-level optimizations. In the first step, we train a powerful universal feature-extractor that is capable of learning important knowledge from large, heterogeneous data sets in various domains. In the second step, we train a task-specific domain-adapter for a new target domain or task with only a limited amount of data available for training. Then the composition of the adapter and the universal feature-extractor effectively explores feature which serve as an important component of image regularization for the new domains, and this leads to high-quality reconstruction despite the data limitation issue. We apply this framework to reconstruct under-sampled MR images with limited data by using a collection of diverse data samples from different domains, such as images of other anatomies, measurements of various sampling ratios, and even different image modalities, including natural images. Experimental results demonstrate a promising transfer learning capability of the proposed method.

Problem

• image reconstruction
• limited training data
• cross-domain
• transfer learning
• MRI

Innovation

• transfer learning
• bi-level optimization
• cross-domain image reconstruction
• universal feature extractor
• domain adapter