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
翻译:光谱压缩成像(CASSI)系统中,超光谱图像(HSI)重建方法被用于从压缩测量中恢复空间光谱信号。在这些算法中,深层开发的方法显示了有希望的性能,但有两个问题。首先,它们没有从高度关联的CASSI中估计降解模式和不良程度,以指导迭代学习。其次,它们主要是CNN的,显示在捕捉远程依赖性方面有局限性。在本文件中,我们提议一个有原则的降解软件不叠加框架(DAUUF)来估计压缩图像和物理遮罩的参数,然后使用这些参数来控制每一次迭代。此外,我们定制了一个新型的半发式半发式(HST),同时捕捉到本地内容和非本地依赖性。通过将HST插入DAUF,我们为HSI的重建建立了第一种基于变压器的深层开发方法,即递增半发式半发变换软件(DAUHHST)。实验显示,DAUHST将大大超过州-SUDS-ODODSTIM/MALAUDLAM 需要更廉价的计算和MAGUDSUDSDSUDS/M/MALMLMLMLMLAMLMLMLMLMAR成本。