Deep priors have emerged as potent methods in hyperspectral image (HSI)
reconstruction. While most methods emphasize space-domain learning using image
space priors like non-local similarity, frequency-domain learning using image
frequency priors remains neglected, limiting the reconstruction capability of
networks. In this paper, we first propose a Hyperspectral Frequency Correlation
(HFC) prior rooted in in-depth statistical frequency analyses of existent HSI
datasets. Leveraging the HFC prior, we subsequently establish the frequency
domain learning composed of a Spectral-wise self-Attention of Frequency (SAF)
and a Spectral-spatial Interaction of Frequency (SIF) targeting low-frequency
and high-frequency components, respectively. The outputs of SAF and SIF are
adaptively merged by a learnable gating filter, thus achieving a thorough
exploitation of image frequency priors. Integrating the frequency domain
learning and the existing space domain learning, we finally develop the
Correlation-driven Mixing Domains Transformer (CMDT) for HSI reconstruction.
Extensive experiments highlight that our method surpasses various
state-of-the-art (SOTA) methods in reconstruction quality and computational
efficiency.

通过在现有高光谱图像数据集上进行深入的统计频率分析，我们提出了以高光谱频率相关性 (HFC) 为根基的频率域学习方法，包括了面向低频和高频分量的频谱自适应自注意力 (SAF) 和频谱 - 空间交互 (SIF)，通过可学习的门控滤波器将 SAF 和 SIF 的输出进行自适应融合，从而实现了对图像频率先验的全面利用，并与现有的空间域学习相结合，最终开发了基于相关性驱动的混合域变换 (CMDT) 方法进行高光谱图像重建，实验证明我们的方法在重建质量和计算效率上超过了各种最新方法。

利用频率相关性进行高光谱图像重建

Exploiting Frequency Correlation for Hyperspectral Image Reconstruction

A broad class of problems at the core of computational imaging, sensing, and
low-level computer vision reduces to the inverse problem of extracting latent
images that follow a prior distribution, from measurements taken under a known
physical image formation model. Traditionally, hand-crafted priors along with
iterative optimization methods have been used to solve such problems. In this
paper we present unrolled optimization with deep priors, a principled framework
for infusing knowledge of the image formation into deep networks that solve
inverse problems in imaging, inspired by classical iterative methods. We show
that instances of the framework outperform the state-of-the-art by a
substantial margin for a wide variety of imaging problems, such as denoising,
deblurring, and compressed sensing magnetic resonance imaging (MRI). Moreover,
we conduct experiments that explain how the framework is best used and why it
outperforms previous methods.

本文提出了一种使用深度先验知识进行解决图像重建问题的框架，该框架在去噪、去模糊以及磁共振成像的压缩感知等多种成像问题上实现了较大幅度的性能提升，并通过实验进一步解释了其优越性。