Hyperparameter Analysis for Derivative Compressive Sampling
This work offers incremental improvements for users of DCS in applications like optical image reconstruction and photometric stereo.
The authors analyzed the sensitivity of derivative compressive sampling (DCS) to hyperparameters using brute-force search on surface image data, providing guidelines to improve signal recovery performance.
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.