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

We present RND-SCI, a novel framework for compressive hyperspectral image
(HSI) reconstruction. Our framework decomposes the reconstructed object into
range-space and null-space components, where the range-space part ensures the
solution conforms to the compression process, and the null-space term
introduces a deep HSI prior to constraining the output to have satisfactory
properties. RND-SCI is not only simple in design with strong interpretability
but also can be easily adapted to various HSI reconstruction networks,
improving the quality of HSIs with minimal computational overhead. RND-SCI
significantly boosts the performance of HSI reconstruction networks in
retraining, fine-tuning or plugging into a pre-trained off-the-shelf model.
Based on the framework and SAUNet, we design an extremely fast HSI
reconstruction network, RND-SAUNet, which achieves an astounding 91 frames per
second while maintaining superior reconstruction accuracy compared to other
less time-consuming methods. Code and models are available at
this https URL

RND-SCI 是一种新颖的压缩高光谱图像 (HSI) 重建框架，通过将重建对象分解为范围空间和零空间组成部分来提高 HIS 重建的质量，并且可以用于各种 HSI 重建网络中，同时设计了一种非常快速的 HSI 重建网络，RND-SAUNet，将帧率提高到 91 帧 / 秒，并保持优秀的重建准确性。

一种快速灵活的光谱压缩成像的范围 - 零空间分解方法

A Range-Null Space Decomposition Approach for Fast and Flexible 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。