Hyperspectral (HS) imaging retrieves information from data obtained across broadband spectral channels. Information to retrieve is a 3D cube, where two coordinates are spatial and the third one is spectral. This cube is complex-valued with varying amplitude and phase. We consider shearography optical setup, in which two phase-shifted broadband copies of the object projections are interfering at a sensor. Registered observations are intensities summarized over spectral channels. For phase reconstruction, the variational setting of the phase retrieval problem is used to derive the iterative algorithm, which includes the original proximity spectral analysis operator and the sparsity modeling of the complex-valued object 3D cube. We resolve the HS phase retrieval problem without random phase coding of wavefronts typical for the most conventional phase retrieval techniques. We show the performance of the algorithm for object phase and thickness imaging in simulation and experimental tests.

中文翻译：

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高光谱相位检索：利用基于稀疏性的复杂域立方滤波器进行光谱空间数据处理

高光谱（HS）成像从跨宽带光谱通道获得的数据中检索信息。要检索的信息是一个3D立方体，其中两个坐标是空间坐标，第三个坐标是光谱坐标。这个立方体是复数值，具有变化的幅度和相位。我们考虑了剪切成像光学装置，其中对象投影的两个相移宽带副本正在干扰传感器。记录的观测值是在频谱通道上汇总的强度。对于相位重建，使用相位检索问题的变分设置来得出迭代算法，该算法包括原始的邻近光谱分析算子和复值对象3D多维数据集的稀疏模型。我们解决了HS相位检索问题，而没有对大多数常规相位检索技术典型的波前进行随机相位编码。我们在仿真和实验测试中展示了用于物体相和厚度成像的算法的性能。