RGB Pre-Training Enhanced Unobservable Feature Latent Diffusion Model for Spectral Reconstruction

TL;DR

This paper introduces a novel spectral reconstruction method that leverages RGB pre-trained latent diffusion models to effectively model unobservable spectral features, achieving state-of-the-art results in hyperspectral image reconstruction.

Contribution

It extends RGB pre-trained latent diffusion models to focus on unobservable spectral features, enabling improved spectral reconstruction from RGB images.

Findings

Achieves state-of-the-art spectral reconstruction performance.

Effectively models unobservable spectral features in a compact latent space.

Improves downstream relighting tasks with enhanced spectral information.

Abstract

Spectral reconstruction (SR) is a crucial problem in image processing that requires reconstructing hyperspectral images (HSIs) from the corresponding RGB images. A key difficulty in SR is estimating the unobservable feature, which encapsulates significant spectral information not captured by RGB imaging sensors. The solution lies in effectively constructing the spectral-spatial joint distribution conditioned on the RGB image to complement the unobservable feature. Since HSIs share a similar spatial structure with the corresponding RGB images, it is rational to capitalize on the rich spatial knowledge in RGB pre-trained models for spectral-spatial joint distribution learning. To this end, we extend the RGB pre-trained latent diffusion model (RGB-LDM) to an unobservable feature LDM (ULDM) for SR. As the RGB-LDM and its corresponding spatial autoencoder (SpaAE) already excel in spatial knowledge, the ULDM can focus on modeling spectral structure. Moreover, separating the unobservable feature from the HSI reduces the redundant spectral information and empowers the ULDM to learn the joint distribution in a compact latent space. Specifically, we propose a two-stage pipeline consisting of spectral structure representation learning and spectral-spatial joint distribution learning to transform the RGB-LDM into the ULDM. In the first stage, a spectral unobservable feature autoencoder (SpeUAE) is trained to extract and compress the unobservable feature into a 3D manifold aligned with RGB space. In the second stage, the spectral and spatial structures are sequentially encoded by the SpeUAE and the SpaAE, respectively. The ULDM is then acquired to model the distribution of the coded unobservable feature with guidance from the corresponding RGB images. Experimental results on SR and downstream relighting tasks demonstrate that our proposed method achieves state-of-the-art performance.