Coarse-Fine Spectral-Aware Deformable Convolution For Hyperspectral Image Reconstruction

• URL: http://arxiv.org/abs/2406.12703v1
• Date: Tue, 18 Jun 2024 15:15:12 GMT
• Title: Coarse-Fine Spectral-Aware Deformable Convolution For Hyperspectral Image Reconstruction
• Authors: Jincheng Yang, Lishun Wang, Miao Cao, Huan Wang, Yinping Zhao, Xin Yuan,
• Abstract summary: We study the inverse problem of Coded Aperture Snapshot Spectral Imaging (CASSI) We propose Coarse-Fine Spectral-Aware Deformable Convolution Network (CFSDCN) Our CFSDCN significantly outperforms previous state-of-the-art (SOTA) methods on both simulated and real HSI datasets.
• Score: 15.537910100051866
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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