导数压缩采样的超参数分析
Hyperparameter Analysis for Derivative Compressive Sampling
摘要：
微分压缩采样（DCS）是一种利用亚奈奎斯特（sub-Nyquist）采样率从空间梯度的测量中重构信号的方法。DCS的应用包括光学图像重建、光度立体和阴影恢复形状。在这项工作中，我们研究了DCS的算法超参数使用暴力搜索算法的敏感性。我们在表面图像的数据集上进行实验，并推导出用户设置超参数值以提高信号恢复性能的指导方针。
英文摘要：
Derivative compressive sampling (DCS) is a signal reconstruction method from measurements of the spatial gradient with sub-Nyquist sampling rate. Applications of DCS include optical image reconstruction, photometric stereo, and shape-from-shading. In this work, we study the sensitivity of DCS with respect to algorithmic hyperparameters using a brute-force search algorithm. We perform experiments on a dataset of surface images and deduce guidelines for the user to setup values for the hyperparameters for improved signal recovery performance.
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