Learning Spectral Diffusion Prior for Hyperspectral Image Reconstruction

TL;DR

This paper introduces a spectral diffusion prior and a spectral prior injector module to enhance hyperspectral image reconstruction, significantly improving detail recovery and outperforming existing methods in experimental evaluations.

Contribution

It proposes a novel spectral diffusion prior learned via diffusion models and a dynamic prior injector to improve high-frequency detail reconstruction in hyperspectral images.

Findings

Outperforms existing methods by about 0.5 dB in reconstruction quality

Effectively captures high-frequency details of hyperspectral images

Enhances performance of two representative HSI reconstruction networks

Abstract

Hyperspectral image (HSI) reconstruction aims to recover 3D HSI from its degraded 2D measurements. Recently great progress has been made in deep learning-based methods, however, these methods often struggle to accurately capture high-frequency details of the HSI. To address this issue, this paper proposes a Spectral Diffusion Prior (SDP) that is implicitly learned from hyperspectral images using a diffusion model. Leveraging the powerful ability of the diffusion model to reconstruct details, this learned prior can significantly improve the performance when injected into the HSI model. To further improve the effectiveness of the learned prior, we also propose the Spectral Prior Injector Module (SPIM) to dynamically guide the model to recover the HSI details. We evaluate our method on two representative HSI methods: MST and BISRNet. Experimental results show that our method outperforms existing networks by about 0.5 dB, effectively improving the performance of HSI reconstruction.