论文标题
降解意识到频谱压缩成像的降解半剃须变压器
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging
论文作者
论文摘要
在编码的光圈快照光谱压缩成像（CASSI）系统中，采用高光谱图像（HSI）重建方法从压缩测量中恢复了空间 - 光谱信号。在这些算法中，深层展开的方法表现出了有希望的表现，但遭受了两个问题的困扰。首先，他们没有从高度相关的卡西（Cassi）估计降解模式和不适合性的程度来指导迭代学习。其次，它们主要基于CNN，显示出捕获长期依赖性的局限性。在本文中，我们提出了一个原则性的退化感知框架（DAUF），该框架（DAUF）从压缩图像和物理掩码中估算参数，然后使用这些参数来控制每个迭代。此外，我们自定义了一种新颖的半剃须变压器（HST），同时捕获本地内容和非本地依赖性。通过将HST插入DAUF，我们为HSI重建建立了第一个基于变压器的深层展开方法，即降解 - 降解 - 降解的半剃须变压器（DAUHST）。实验表明，Dauhst显着超过了最新方法，同时需要廉价的计算和存储成本。代码和模型将在https://github.com/caiyuanhao1998/mst上发布
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 https://github.com/caiyuanhao1998/MST
