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: this https URL

我们提出了一种适用于高光谱图像重建的像素自适应深度展开变换 (PADUT)，通过引入非局部光谱变换 (NST) 强调了 HSI 的 3D 特征，并通过快速傅里叶变换 (FFT) 改进了阶段交互。实验结果表明我们的方法相较于最先进的 HSI 重建方法具有卓越的性能。