We present a novel acquisition scheme based on a dual-disperser architecture, which can reconstruct a hyperspectral datacube using many times fewer acquisitions than spectral bands. The reconstruction algorithm follows a quadratic regularization approach, based on the assumption that adjacent pixels in the scene share similar spectra, and, if they do not, this corresponds to an edge that is detectable on the panchromatic image. A digital micro-mirror device applies reconfigurable spectral–spatial filtering to the scene for each acquisition, and the filtering code is optimized considering the physical properties of the system. The algorithm is tested on simple multi-spectral scenes with 110 wavelength bands and is able to accurately reconstruct the hyperspectral datacube using only 10 acquisitions.

中文翻译：

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使用双分散器系统优化的编码孔径，用于节俭高光谱图像恢复

我们提出了一种基于双色散体系结构的新颖采集方案，该方案可以使用比光谱带少很多倍的采集来重建高光谱数据立方体。重建算法遵循二次正则化方法，基于以下假设：场景中的相邻像素共享相似的光谱，如果不相似，则对应于在全色图像上可检测到的边缘。数字微镜设备对每次采集将可重新配置的光谱空间滤波应用于场景，并且考虑系统的物理属性来优化滤波代码。该算法在具有110个波段的简单多光谱场景上进行了测试，并且仅使用10次采集就能够准确地重建高光谱数据立方体。