Leveraging pretrained RGB denoisers for hyperspectral image restoration

2026-05-23

AI Summary

This work addresses the challenge of hyperspectral image restoration, which is hindered by data scarcity, sensor-specific characteristics, and the high dimensionality of spectral information, making it difficult to learn robust priors. The authors propose a lightweight transfer framework that projects hyperspectral data into a low-dimensional subspace, leverages a frozen pre-trained RGB denoiser for noise removal, and reconstructs the hyperspectral cube through a lightweight adapter coupled with constrained linear aggregation. This approach is the first to efficiently transfer large-scale RGB image priors to hyperspectral restoration tasks, achieving plug-and-play performance with minimal training. It consistently outperforms specialized hyperspectral methods across multiple datasets in denoising, deblurring, and super-resolution, demonstrating the remarkable transferability of RGB-based priors.

Abstract

Hyperspectral image restoration faces several challenges, including limited training data, strong sensor specificity, and high spectral dimensionality. These limitations hinder the learning of robust hyperspectral priors, motivating the reuse of priors learned from large-scale RGB data. In this work, we propose a minimally trained, lightweight adapter that repurposes frozen pretrained RGB denoisers for hyperspectral restoration through a projection mapping. The method denoises low-dimensional spectral projections and reconstructs the hyperspectral cube through constrained linear aggregation, while preserving plug-and-play compatibility and the stability properties of the underlying RGB denoiser. Experiments on denoising, deblurring, and super-resolution across multiple datasets demonstrate consistent improvements over hyperspectral-specific baselines, showing the strong transferability of large-scale RGB priors.

Problem

• hyperspectral image restoration
• limited training data
• sensor specificity
• high spectral dimensionality
• robust priors

Innovation

• hyperspectral image restoration
• pretrained RGB denoiser
• projection mapping
• transfer learning
• plug-and-play