Coarse-Fine Spectral-Aware Deformable Convolution For Hyperspectral Image Reconstruction
<|reference_start|>Coarse-Fine Spectral-Aware Deformable Convolution For Hyperspectral Image Reconstruction: We study the inverse problem of Coded Aperture Snapshot Spectral Imaging (CASSI), which captures a spatial-spectral data cube using snapshot 2D measurements and uses algorithms to reconstruct 3D hyperspectral images (HSI). However, current methods based on Convolutional Neural Networks (CNNs) struggle to capture long-range dependencies and non-local similarities. The recently popular Transformer-based methods are poorly deployed on downstream tasks due to the high computational cost caused by self-attention. In this paper, we propose Coarse-Fine Spectral-Aware Deformable Convolution Network (CFSDCN), applying deformable convolutional networks (DCN) to this task for the first time. Considering the sparsity of HSI, we design a deformable convolution module that exploits its deformability to capture long-range dependencies and non-local similarities. In addition, we propose a new spectral information interaction module that considers both coarse-grained and fine-grained spectral similarities. Extensive experiments demonstrate that our CFSDCN significantly outperforms previous state-of-the-art (SOTA) methods on both simulated and real HSI datasets.<|reference_end|>
@article{yang2024coarse-fine,
    title={Coarse-Fine Spectral-Aware Deformable Convolution For Hyperspectral
  Image Reconstruction},
    author={Jincheng Yang, Lishun Wang, Miao Cao, Huan Wang, Yinping Zhao, Xin
  Yuan},
    journal={arXiv preprint arXiv:2406.12703},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12703},
    primaryClass={eess.IV cs.CV}
}