用扩散模型学习光谱先验，提升高光谱图像重建细节
Learning Spectral Diffusion Prior for Hyperspectral Image Reconstruction
- 通过扩散模型隐式学习光谱先验，捕捉高频细节
- 在MST和BISRNet上提升约0.5 dB，显著改善重建质量
- 适合需要高精度细节重建的遥感与医学成像场景
高光谱图像（HSI）重建旨在从退化的2D测量中恢复3D HSI。尽管基于深度学习的方法取得了显著进展，但这些方法往往难以准确捕捉HSI的高频细节。为此，本文提出一种隐式学习自高光谱图像的光谱扩散先验（SDP），利用扩散模型强大的细节重建能力，将其注入HSI重建模型可显著提升性能。为进一步增强先验效果，我们还设计了光谱先验注入模块（SPIM），动态引导模型恢复细节。在MST和BISRNet两个代表性方法上进行评估，实验结果表明，本方法相比现有网络性能提升约0.5 dB，有效提升了高光谱图像重建效果。
原文摘要 · Abstract (English)
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.
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