SHARE: A Fully Unsupervised Framework for Single Hyperspectral Image Restoration

2026-01-20

AI Summary

This work addresses the challenge of generalizing hyperspectral image restoration in real-world scenarios where ground-truth labels are unavailable. To this end, we propose SHARE, a novel framework that, for the first time, integrates geometric equivariance constraints with low-rank spectral priors to enable fully unsupervised, self-supervised restoration from a single hyperspectral image. SHARE enforces equivariant consistency through differentiable geometric transformations—such as rotation and scaling—and introduces a dynamic, adaptive spectral attention mechanism to effectively model both global low-rank structure and local spectral-spatial correlations. Extensive experiments demonstrate that SHARE significantly outperforms existing unsupervised methods on both inpainting and super-resolution tasks, achieving performance close to that of supervised models, thereby confirming its effectiveness and practical utility.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

Hyperspectral image (HSI) restoration is a fundamental challenge in computational imaging and computer vision. It involves ill-posed inverse problems, such as inpainting and super-resolution. Although deep learning methods have transformed the field through data-driven learning, their effectiveness hinges on access to meticulously curated ground-truth datasets. This fundamentally restricts their applicability in real-world scenarios where such data is unavailable. This paper presents SHARE (Single Hyperspectral Image Restoration with Equivariance), a fully unsupervised framework that unifies geometric equivariance principles with low-rank spectral modelling to eliminate the need for ground truth. SHARE's core concept is to exploit the intrinsic invariance of hyperspectral structures under differentiable geometric transformations (e.g. rotations and scaling) to derive self-supervision signals through equivariance consistency constraints. Our novel Dynamic Adaptive Spectral Attention (DASA) module further enhances this paradigm shift by explicitly encoding the global low-rank property of HSI and adaptively refining local spectral-spatial correlations through learnable attention mechanisms. Extensive experiments on HSI inpainting and super-resolution tasks demonstrate the effectiveness of SHARE. Our method outperforms many state-of-the-art unsupervised approaches and achieves performance comparable to that of supervised methods. We hope that our approach will shed new light on HSI restoration and broader scientific imaging scenarios. The code will be released at https://github.com/xuwayyy/SHARE.

Problem

• Hyperspectral image restoration
• unsupervised learning
• ill-posed inverse problems
• ground-truth dependency
• real-world applicability

Innovation

• unsupervised hyperspectral restoration
• geometric equivariance
• low-rank spectral modeling
• self-supervision
• Dynamic Adaptive Spectral Attention