Hyperspectral Image (HSI) reconstruction has made gratifying progress with the deep unfolding framework by formulating the problem into a data module and a prior module. Nevertheless, existing methods still face the problem of insufficient matching with HSI data. The issues lie in three aspects: 1) fixed gradient descent step in the data module while the degradation of HSI is agnostic in the pixel-level. 2) inadequate prior module for 3D HSI cube. 3) stage interaction ignoring the differences in features at different stages. To address these issues, in this work, we propose a Pixel Adaptive Deep Unfolding Transformer (PADUT) for HSI reconstruction. In the data module, a pixel adaptive descent step is employed to focus on pixel-level agnostic degradation. In the prior module, we introduce the Non-local Spectral Transformer (NST) to emphasize the 3D characteristics of HSI for recovering. Moreover, inspired by the diverse expression of features in different stages and depths, the stage interaction is improved by the Fast Fourier Transform (FFT). Experimental results on both simulated and real scenes exhibit the superior performance of our method compared to state-of-the-art HSI reconstruction methods. The code is released at: https://github.com/MyuLi/PADUT.

翻译：高光谱图像（HSI）重建借助深度展开框架，通过将问题分解为数据模块和先验模块，取得了令人瞩目的进展。然而，现有方法仍面临与HSI数据匹配不足的问题。问题体现在三个方面：1）数据模块中采用固定的梯度下降步长，而HSI退化在像素层面上是未知的；2）针对三维HSI立方体的先验模块不够充分；3）阶段交互忽略了不同阶段特征的差异性。为解决这些问题，本文提出了一种用于高光谱图像重建的像素自适应深度展开Transformer（PADUT）。在数据模块中，采用像素自适应下降步长来聚焦于像素层面的未知退化。在先验模块中，引入非局部光谱Transformer（NST）以强调HSI的三维特性进行恢复。此外，受不同阶段和深度特征多样性表达的启发，利用快速傅里叶变换（FFT）改进了阶段交互。在模拟场景和真实场景上的实验结果表明，与最先进的HSI重建方法相比，我们的方法具有更优越的性能。代码已发布在：https://github.com/MyuLi/PADUT。