Deep unfolding methods have made impressive progress in restoring 3D
hyperspectral images (HSIs) from 2D measurements through convolution neural
networks or Transformers in spectral compressive imaging. However, they cannot
efficiently capture long-range dependencies using global receptive fields,
which significantly limits their performance in HSI reconstruction. Moreover,
these methods may suffer from local context neglect if we directly utilize
Mamba to unfold a 2D feature map as a 1D sequence for modeling global
long-range dependencies. To address these challenges, we propose a novel Dual
Hyperspectral Mamba (DHM) to explore both global long-range dependencies and
local contexts for efficient HSI reconstruction. After learning informative
parameters to estimate degradation patterns of the CASSI system, we use them to
scale the linear projection and offer noise level for the denoiser (i.e., our
proposed DHM). Specifically, our DHM consists of multiple dual hyperspectral S4
blocks (DHSBs) to restore original HSIs. Particularly, each DHSB contains a
global hyperspectral S4 block (GHSB) to model long-range dependencies across
the entire high-resolution HSIs using global receptive fields, and a local
hyperspectral S4 block (LHSB) to address local context neglect by establishing
structured state-space sequence (S4) models within local windows. Experiments
verify the benefits of our DHM for HSI reconstruction. The source codes and
models will be available at this https URL

提出了一种新颖的双重高光谱 Mamba (DHM) 方法，通过探索全局远程依赖和局部环境，实现高效的 HSI 重建。

双波段高光谱蛇纹红法进行高效光谱压缩成像

Dual Hyperspectral Mamba for Efficient Spectral Compressive Imaging

In coded aperture snapshot spectral compressive imaging (CASSI) systems,
hyperspectral image (HSI) reconstruction methods are employed to recover the
spatial-spectral signal from a compressed measurement. Among these algorithms,
deep unfolding methods demonstrate promising performance but suffer from two
issues. Firstly, they do not estimate the degradation patterns and
ill-posedness degree from the highly related CASSI to guide the iterative
learning. Secondly, they are mainly CNN-based, showing limitations in capturing
long-range dependencies. In this paper, we propose a principled
Degradation-Aware Unfolding Framework (DAUF) that estimates parameters from the
compressed image and physical mask, and then uses these parameters to control
each iteration. Moreover, we customize a novel Half-Shuffle Transformer (HST)
that simultaneously captures local contents and non-local dependencies. By
plugging HST into DAUF, we establish the first Transformer-based deep unfolding
method, Degradation-Aware Unfolding Half-Shuffle Transformer (DAUHST), for HSI
reconstruction. Experiments show that DAUHST significantly surpasses
state-of-the-art methods while requiring cheaper computational and memory
costs. Code and models will be released at
this https URL

本文提出一个基于 Transformer 网络的 HSI 重建方法，称作 DAUHST。该方法使用 DAUF 框架估算压缩图像和物理掩模的参数，并使用这些参数来控制每一次迭代。同时，自定义的半洗牌 Transformer（HST）可以同时捕获局部内容和非局部依赖性，实验证明 DAUHST 明显优于其他最先进的方法，并具有较低的计算和存储成本。

面向降级的展开半混洗变压器用于光谱压缩成像

Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral  Compressive Imaging

Most compressive sensing (CS) reconstruction methods can be divided into two
categories, i.e. model-based methods and classical deep network methods. By
unfolding the iterative optimization algorithm for model-based methods onto
networks, deep unfolding methods have the good interpretation of model-based
methods and the high speed of classical deep network methods. In this paper, to
solve the visual image CS problem, we propose a deep unfolding model dubbed
AMP-Net. Rather than learning regularization terms, it is established by
unfolding the iterative denoising process of the well-known approximate message
passing algorithm. Furthermore, AMP-Net integrates deblocking modules in order
to eliminate the blocking artifacts that usually appear in CS of visual images.
In addition, the sampling matrix is jointly trained with other network
parameters to enhance the reconstruction performance. Experimental results show
that the proposed AMP-Net has better reconstruction accuracy than other
state-of-the-art methods with high reconstruction speed and a small number of
network parameters.

本文提出了一种深度展开模型 AMC-Net 来解决视觉图像的压缩感知重构问题，通过展开迭代卷积神经网络来模拟近似消息传递算法的去噪过程，同时集成去块模块以消除在图像压缩感知中经常出现的块状伪影，并联合训练采样矩阵和其他网络参数以提高重构精度。实验结果表明，该方法在重构精度、速度和参数数量等方面均优于其他先进方法。