Abstract A novel algorithm for reconstruction of hyperspectral 3D complex domain images (phase/amplitude) from noisy complex domain observations has been developed and studied. This algorithm starts from the SVD (singular value decomposition) analysis of the observed complex-valued data and looks for the optimal low dimension eigenspace. These eigenspace images are processed based on special non-local block-matching complex domain filters. The accuracy and quantitative advantage of the new algorithm for phase and amplitude imaging are demonstrated in simulation tests and in processing of the experimental data. It is shown that the algorithm is effective and provides reliable results even for highly noisy data.

中文翻译：

---

基于复值特征子空间去噪的高光谱相位成像

摘要 已经开发和研究了一种用于从嘈杂的复杂域观测重建高光谱 3D 复杂域图像（相位/幅度）的新算法。该算法从观察到的复值数据的 SVD（奇异值分解）分析开始，寻找最优的低维特征空间。这些特征空间图像基于特殊的非局部块匹配复杂域过滤器进行处理。在仿真测试和实验数据处理中，证明了新算法在相位和幅度成像方面的准确性和定量优势。结果表明，该算法是有效的，即使对于高噪声数据也能提供可靠的结果。