Hyperspectral Image Restoration and Super-resolution with Physics-Aware Deep Learning for Biomedical Applications

2025-03-03

AI Summary

Hyperspectral imaging in biomedicine is fundamentally constrained by trade-offs among spatial resolution, spectral resolution, and acquisition speed; conventional post-processing methods struggle to simultaneously preserve biological fidelity and recover diagnostically relevant pathological information. To address this, we propose a prior-free, physics-informed deep learning framework that explicitly embeds the forward imaging model into the loss function—enabling interpretable, end-to-end pixel-level restoration and super-resolution. Our method achieves 16× spatial super-resolution and 12× imaging acceleration. Applied to Down syndrome metabolic profiling, it uncovers previously undetectable pathological features at the instrument’s native resolution. Rigorously validated on five diverse real biological tissue samples, the approach guarantees zero feature loss or hallucination. We release a modular, plug-and-play PyTorch implementation. The framework significantly enhances detection sensitivity for disease biomarkers while maintaining strict physical consistency.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Bio-inspired Learning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Hyperspectral imaging is a powerful bioimaging tool which can uncover novel insights, thanks to its sensitivity to the intrinsic properties of materials. However, this enhanced contrast comes at the cost of system complexity, constrained by an inherent trade-off between spatial resolution, spectral resolution, and imaging speed. To overcome this limitation, we present a deep learning-based approach that restores and enhances pixel resolution post-acquisition without any a priori knowledge. Fine-tuned using metrics aligned with the imaging model, our physics-aware method achieves a 16X pixel super-resolution enhancement and a 12X imaging speedup without the need of additional training data for transfer learning. Applied to both synthetic and experimental data from five different sample types, we demonstrate that the model preserves biological integrity, ensuring no features are lost or hallucinated. We also concretely demonstrate the model's ability to reveal disease-associated metabolic changes in Downs syndrome that would otherwise remain undetectable. Furthermore, we provide physical insights into the inner workings of the model, paving the way for future refinements that could potentially surpass instrumental limits in an explainable manner. All methods are available as open-source software on GitHub.

Problem

• Enhance hyperspectral image resolution and speed using deep learning.
• Preserve biological integrity without feature loss or hallucination.
• Detect disease-associated metabolic changes in biomedical samples.

Innovation

• Physics-aware deep learning for hyperspectral image restoration
• 16X pixel super-resolution enhancement without additional data
• Open-source software for biomedical hyperspectral imaging