Abstract

The snapshot hyperspectral imaging is an emerging technique with numerous applications. However, the hyperspectral imaging reconstruction is often time-consuming, which is placing a limit on the development of snapshot hyperspectral imaging. We present an efficient reconstruction algorithm based on the tensor analysis and the low-rank constraint. The hyperspectral data cube is regarded as a low rank three-order tensor, which can jointly treat both spatial and spectral modes. The 3D-LRC method can greatly decrease the computation time without unfolding the hyperspectral data cube into 2D patches. Compared with the-state-of-the-art method, the proposed method has a great improvement in the reconstruction speed and quality. The method has been implemented on two typical snapshot hyperspectral imaging systems.

摘要

快照高光谱成像是一种具有众多应用的新兴技术。然而，高光谱成像重建往往非常耗时，这限制了快照高光谱成像的发展。我们提出了一种基于张量分析和低秩约束的高效重建算法。高光谱数据立方体被认为是一个低秩的三阶张量，它可以联合处理空间和光谱模式。3D-LRC 方法可以大大减少计算时间，而无需将高光谱数据立方体展开成 2D 块。与最先进的方法相比，所提出的方法在重建速度和质量上都有很大的提高。该方法已在两个典型的快照高光谱成像系统上实现。