Tian Haijun, Zhao Jufeng, Zhu Junjie, Tang Xuanji, Cui Guangmang, Hou Changlun
Appl Opt. 2023 May 10;62(14):3649-3659. doi: 10.1364/AO.483993.
Coded aperture snapshot spectral imaging (CASSI) aims to capture the high-dimensional (usually 3D) data cube using a 2D sensor in a single snapshot. Due to the ill-posed snapshot, the reconstruction results are not ideal. One feasible solution is to utilize additional information such as the panchromatic measurement in CASSI. In this paper, we propose a dual-camera hyperspectral reconstruction method based on the deep image prior (DIP) and a guided filter. In particular, the panchromatic measurements are used to estimate spatial detail, and spectral details are provided using CASSI measurements. These measurements are used as a priori learning by the self-supervised network. Using iteration combined with DIP, the hyperspectral reconstruction is continuously updated iteratively. Finally, the panchromatic measurement is used as the guidance image, and the reconstruction result is optimized by guide filtering. A large number of experimental results demonstrate that our method without training data can reconstruct spectral data with both high spectral accuracy and spatial resolution.
编码孔径快照光谱成像（CASSI）旨在通过二维传感器在单次快照中捕获高维（通常为三维）数据立方体。由于快照的不适定性，重建结果并不理想。一种可行的解决方案是利用诸如CASSI中的全色测量等附加信息。在本文中，我们提出了一种基于深度图像先验（DIP）和引导滤波器的双相机高光谱重建方法。具体而言，全色测量用于估计空间细节，而光谱细节则通过CASSI测量提供。这些测量被自监督网络用作先验学习。通过结合DIP的迭代，高光谱重建不断迭代更新。最后，将全色测量用作引导图像，并通过引导滤波对重建结果进行优化。大量实验结果表明，我们的方法无需训练数据即可重建具有高光谱精度和空间分辨率的光谱数据。
