Haze contamination in hyperspectral remote sensing images (HSI) can lead to
spatial visibility degradation and spectral distortion. Haze in HSI exhibits
spatial irregularity and inhomogeneous spectral distribution, with few dehazing
networks available. Current CNN and Transformer-based dehazing methods fail to
balance global scene recovery, local detail retention, and computational
efficiency. Inspired by the ability of Mamba to model long-range dependencies
with linear complexity, we explore its potential for HSI dehazing and propose
the first HSI Dehazing Mamba (HDMba) network. Specifically, we design a novel
window selective scan module (WSSM) that captures local dependencies within
windows and global correlations between windows by partitioning them. This
approach improves the ability of conventional Mamba in local feature
extraction. By modeling the local and global spectral-spatial information flow,
we achieve a comprehensive analysis of hazy regions. The DehazeMamba layer
(DML), constructed by WSSM, and residual DehazeMamba (RDM) blocks, composed of
DMLs, are the core components of the HDMba framework. These components
effectively characterize the complex distribution of haze in HSIs, aiding in
scene reconstruction and dehazing. Experimental results on the Gaofen-5 HSI
dataset demonstrate that HDMba outperforms other state-of-the-art methods in
dehazing performance. The code will be available at
this https URL

基于长距离依赖模型 Mamba，本研究提出了第一个用于高光谱图像去霾的 HSI Dehazing Mamba (HDMba) 网络，通过设计了一个新颖的窗口选择性扫描模块 (WSSM)，以及由 WSSM 和残差 DehazeMamba (RDM) 块构成的 DehazeMamba layer (DML)，实现了对复杂的雾霾分布的准确建模，从而在场景重建和去霾方面取得了优于其他最先进方法的效果。

HDMba: 带状态空间模型的高光谱遥感图像去雾

HDMba: Hyperspectral Remote Sensing Imagery Dehazing with State Space  Model

Hyperspectral remote sensing images (HSIs) usually have high spectral
resolution and low spatial resolution. Conversely, multispectral images (MSIs)
usually have low spectral and high spatial resolutions. The problem of
inferring images which combine the high spectral and high spatial resolutions
of HSIs and MSIs, respectively, is a data fusion problem that has been the
focus of recent active research due to the increasing availability of HSIs and
MSIs retrieved from the same geographical area.
We formulate this problem as the minimization of a convex objective function
containing two quadratic data-fitting terms and an edge-preserving regularizer.
The data-fitting terms account for blur, different resolutions, and additive
noise. The regularizer, a form of vector Total Variation, promotes
piecewise-smooth solutions with discontinuities aligned across the
hyperspectral bands.
The downsampling operator accounting for the different spatial resolutions,
the non-quadratic and non-smooth nature of the regularizer, and the very large
size of the HSI to be estimated lead to a hard optimization problem. We deal
with these difficulties by exploiting the fact that HSIs generally "live" in a
low-dimensional subspace and by tailoring the Split Augmented Lagrangian
Shrinkage Algorithm (SALSA), which is an instance of the Alternating Direction
Method of Multipliers (ADMM), to this optimization problem, by means of a
convenient variable splitting. The spatial blur and the spectral linear
operators linked, respectively, with the HSI and MSI acquisition processes are
also estimated, and we obtain an effective algorithm that outperforms the
state-of-the-art, as illustrated in a series of experiments with simulated and
real-life data.

该研究介绍了一种基于凸优化的构建高分辨率 HSI 和高分辨率 MSI 综合图像的数据融合方法，使用向量全变差作为正则化项，通过 Split Augmented Lagrangian Shrinkage Algorithm（SALSA）算法实现优化，取得了优于现有算法的效果。