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Abstract
En 中文
Hyperspectral image (HSI) reconstruction refers to the process of recovering the high-dimensional HSI signal from the measurements captured by various imaging systems. In the case of the coded aperture snapshot spectral imaging (CASSI) system, this involves recovering the HSI signal from snapshot measurements obtained using a coded aperture and disperser. However, previous methods for HSI reconstruction have been limited by the challenges of reconstructing complex high-dimensional data and the inevitable noise present in the measurements. To better capture the complex distribution of high-dimensional data and mitigate the impact of noise on reconstruction performance, this paper introduces an end-to-end approach leveraging a diffusion model, termed the hierarchically conditional diffusion model for HSI reconstruction (HDiff-HIR). HDiff-HIR achieves high-quality reconstruction by initializing with pure Gaussian noise and using a network to iteratively refine it. Additionally, we design a condition generation module, called the mask-integrated condition generation module (MCGM), which integrates 2D measurements with the coded aperture of the imaging system as conditions and hierarchically embeds them into the denoising network. Furthermore, within the network, we introduce a novel self-attention mechanism, named local-global spectral-enhanced multi-head self-attention (LGS-MSA), to efficiently capture long-range spatial dependencies in HSIs at relatively modest computational costs while incorporating fine-grained spectral features as complementary information. In LGS-MSA, we incorporate time embeddings to make it time-dependent, enabling it to capture both long-range spatial and temporal dependencies simultaneously. Through comprehensive experiments on both simulated and real datasets, we demonstrate that HDiff-HIR not only outperforms other advanced methods but also exhibits strong generalization capability. The code of HDiff-HIR is accessible: https://github.com/chenx2000/HDiff-HIR
Keywords:
Hyperspectral image reconstruction
snapshot compressive imaging
generative model
multi-head self-attention
Journal
IF:
11.1
Papers:
612
Citations:
3.1W
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