An approach to parameter optimization for the low-rank matrix recovery method in hyperspectral imaging is discussed. We formulate an optimization problem with respect to the initial parameters of the low-rank matrix recovery method. The performance for different parameter settings is compared in terms of computational times and memory. The results are evaluated by computing the peak signal-to-noise ratio as a quantitative measure. The potential improvement of the performance of the noise reduction method is discussed when optimizing the choice of the initial values. The optimization method is tested on standard and openly available hyperspectral data sets including Indian Pines, Pavia Centre, and Pavia University.

翻译：针对高光谱成像中低秩矩阵恢复方法的参数优化问题进行了探讨。我们构建了以低秩矩阵恢复方法初始参数为变量的优化问题，并从计算时间和内存占用两方面比较了不同参数配置的性能表现。通过计算峰值信噪比作为定量评价指标对结果进行评估，同时讨论了优化初始值选择对降噪方法性能的潜在改进效果。该优化方法已在包括Indian Pines、Pavia Centre和Pavia University在内的标准公开高光谱数据集上进行了测试验证。