Chen Yurong, Wang Yaonan, Zhang Hui
IEEE Trans Pattern Anal Mach Intell. 2023 Sep;45(9):11096-11107. doi: 10.1109/TPAMI.2023.3265749. Epub 2023 Aug 7.
Spectral images with rich spatial and spectral information have wide usage, however, traditional spectral imaging techniques undeniably take a long time to capture scenes. We consider the computational imaging problem of the snapshot spectral spectrometer, i.e., the Coded Aperture Snapshot Spectral Imaging (CASSI) system. For the sake of a fast and generalized reconstruction algorithm, we propose a prior image guidance-based snapshot compressive imaging method. Typically, the prior image denotes the RGB measurement captured by the additional uncoded panchromatic camera of the dual-camera CASSI system. We argue that the RGB image as a prior image can provide valuable semantic information. More importantly, we design the Prior Image Semantic Similarity (PIDS) regularization term to enhance the reconstructed spectral image fidelity. In particular, the PIDS is formulated as the difference between the total variation of the prior image and the recovered spectral image. Then, we solve the PIDS regularized reconstruction problem by the Alternating Direction Method of Multipliers (ADMM) optimization algorithm. Comprehensive experiments on various datasets demonstrate the superior performance of our method.
具有丰富空间和光谱信息的光谱图像有广泛的用途，然而，传统光谱成像技术不可否认地需要很长时间来捕捉场景。我们考虑快照光谱仪的计算成像问题，即编码孔径快照光谱成像（CASSI）系统。为了获得一种快速且通用的重建算法，我们提出一种基于先验图像引导的快照压缩成像方法。通常，先验图像表示由双相机CASSI系统的附加未编码全色相机捕获的RGB测量值。我们认为RGB图像作为先验图像可以提供有价值的语义信息。更重要的是，我们设计了先验图像语义相似性（PIDS）正则化项来提高重建光谱图像的保真度。特别地，PIDS被表述为先验图像和恢复的光谱图像的总变差之间的差异。然后，我们通过交替方向乘子法（ADMM）优化算法来解决PIDS正则化重建问题。在各种数据集上进行的综合实验证明了我们方法的卓越性能。
