IEEE Trans Pattern Anal Mach Intell. 2017 Oct;39(10):2104-2111. doi: 10.1109/TPAMI.2016.2621050. Epub 2016 Oct 25.
Leveraging the compressive sensing (CS) theory, coded aperture snapshot spectral imaging (CASSI) provides an efficient solution to recover 3D hyperspectral data from a 2D measurement. The dual-camera design of CASSI, by adding an uncoded panchromatic measurement, enhances the reconstruction fidelity while maintaining the snapshot advantage. In this paper, we propose an adaptive nonlocal sparse representation (ANSR) model to boost the performance of dual-camera compressive hyperspectral imaging (DCCHI). Specifically, the CS reconstruction problem is formulated as a 3D cube based sparse representation to make full use of the nonlocal similarity in both the spatial and spectral domains. Our key observation is that, the panchromatic image, besides playing the role of direct measurement, can be further exploited to help the nonlocal similarity estimation. Therefore, we design a joint similarity metric by adaptively combining the internal similarity within the reconstructed hyperspectral image and the external similarity within the panchromatic image. In this way, the fidelity of CS reconstruction is greatly enhanced. Both simulation and hardware experimental results show significant improvement of the proposed method over the state-of-the-art.
利用压缩感知 (CS) 理论，编码孔径快照光谱成像 (CASSI) 提供了一种从二维测量中恢复 3D 高光谱数据的有效解决方案。CASSI 的双相机设计通过增加未编码的全色测量，在保持快照优势的同时提高了重建保真度。在本文中，我们提出了一种自适应非局部稀疏表示 (ANSR) 模型，以提高双相机压缩高光谱成像 (DCCHI) 的性能。具体来说，CS 重建问题被表述为基于 3D 立方体的稀疏表示，以充分利用空间和光谱域中的非局部相似性。我们的关键观察是，全色图像除了扮演直接测量的角色外，还可以进一步利用来帮助非局部相似性估计。因此，我们通过自适应地组合重建高光谱图像内部的相似性和全色图像内部的相似性来设计联合相似性度量。通过这种方式，CS 重建的保真度得到了极大的提高。模拟和硬件实验结果均表明，与现有方法相比，所提出的方法有了显著的改进。
