Exploiting Frequency Correlation for Hyperspectral Image Reconstruction
<|reference_start|>Exploiting Frequency Correlation for Hyperspectral Image Reconstruction: Deep priors have emerged as potent methods in hyperspectral image (HSI) reconstruction. While most methods emphasize space-domain learning using image space priors like non-local similarity, frequency-domain learning using image frequency priors remains neglected, limiting the reconstruction capability of networks. In this paper, we first propose a Hyperspectral Frequency Correlation (HFC) prior rooted in in-depth statistical frequency analyses of existent HSI datasets. Leveraging the HFC prior, we subsequently establish the frequency domain learning composed of a Spectral-wise self-Attention of Frequency (SAF) and a Spectral-spatial Interaction of Frequency (SIF) targeting low-frequency and high-frequency components, respectively. The outputs of SAF and SIF are adaptively merged by a learnable gating filter, thus achieving a thorough exploitation of image frequency priors. Integrating the frequency domain learning and the existing space domain learning, we finally develop the Correlation-driven Mixing Domains Transformer (CMDT) for HSI reconstruction. Extensive experiments highlight that our method surpasses various state-of-the-art (SOTA) methods in reconstruction quality and computational efficiency.<|reference_end|>
@article{yan2024exploiting,
    title={Exploiting Frequency Correlation for Hyperspectral Image Reconstruction},
    author={Muge Yan, and Lizhi Wang, and Lin Zhu, and Hua Huang},
    journal={arXiv preprint arXiv:2406.00683},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.00683},
    primaryClass={eess.IV cs.CV cs.MM}
}