Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction

TL;DR

This paper introduces CST, a novel sparse Transformer model that effectively reconstructs hyperspectral images from compressed measurements by leveraging spectral-aware patch selection and self-similarity, outperforming existing methods.

Contribution

The paper proposes a coarse-to-fine sparse Transformer with spectral-aware screening and customized attention for improved hyperspectral image reconstruction.

Findings

CST outperforms state-of-the-art methods in accuracy.

CST requires less computational cost.

Effective modeling of spectral and spatial features.

Abstract

Many algorithms have been developed to solve the inverse problem of coded aperture snapshot spectral imaging (CASSI), i.e., recovering the 3D hyperspectral images (HSIs) from a 2D compressive measurement. In recent years, learning-based methods have demonstrated promising performance and dominated the mainstream research direction. However, existing CNN-based methods show limitations in capturing long-range dependencies and non-local self-similarity. Previous Transformer-based methods densely sample tokens, some of which are uninformative, and calculate the multi-head self-attention (MSA) between some tokens that are unrelated in content. This does not fit the spatially sparse nature of HSI signals and limits the model scalability. In this paper, we propose a novel Transformer-based method, coarse-to-fine sparse Transformer (CST), firstly embedding HSI sparsity into deep learning for HSI reconstruction. In particular, CST uses our proposed spectra-aware screening mechanism (SASM) for coarse patch selecting. Then the selected patches are fed into our customized spectra-aggregation hashing multi-head self-attention (SAH-MSA) for fine pixel clustering and self-similarity capturing. Comprehensive experiments show that our CST significantly outperforms state-of-the-art methods while requiring cheaper computational costs. The code and models will be released at https://github.com/caiyuanhao1998/MST