Song Hongzhen, Hou Qifeng, Sun Kaipeng, Zhang Guixiang, Xu Tuoqi, Sun Benjin, Zhang Liu
College of Instrumentation & Electrical Engineering, Jilin University, Changchun 130061, China.
School of Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
Sensors (Basel). 2025 Jul 23;25(15):4556. doi: 10.3390/s25154556.
Computational imaging spectrometers using broad-bandpass filter arrays with distinct transmission functions are promising implementations of miniaturization. The number of filters is limited by the practical factors. Compressed sensing is used to model the system as linear underdetermined equations for hyperspectral imaging. This paper proposes the following method: parallel dictionary reconstruction and fusion for spectral recovery in computational imaging spectrometers. Orthogonal systems are the dictionary candidates for reconstruction. According to observation of ground objects, the dictionaries are selected from the candidates using the criterion of incoherence. Parallel computations are performed with the selected dictionaries, and spectral recovery is achieved by fusion of the computational results. The method is verified by simulating visible-NIR spectral recovery of typical ground objects. The proposed method has a mean square recovery error of ≤1.73 × 10 and recovery accuracy of ≥0.98 and is both more universal and more stable than those of traditional sparse representation methods.
使用具有不同传输函数的宽带通滤波器阵列的计算成像光谱仪是实现小型化的有前景的方案。滤波器的数量受到实际因素的限制。压缩感知被用于将该系统建模为用于高光谱成像的线性欠定方程组。本文提出了以下方法：计算成像光谱仪中用于光谱恢复的并行字典重建与融合。正交系统是重建的字典候选。根据对地物的观测，使用非相干性准则从候选中选择字典。对所选字典进行并行计算，并通过融合计算结果实现光谱恢复。通过模拟典型地物的可见 - 近红外光谱恢复对该方法进行了验证。所提出的方法具有均方恢复误差≤1.73×10 且恢复精度≥0.98，并且比传统稀疏表示方法更具通用性和稳定性。
