Hyperparameter Analysis for Derivative Compressive Sampling

TL;DR

This paper investigates how hyperparameters affect derivative compressive sampling (DCS) performance, providing practical guidelines for parameter selection to enhance signal reconstruction in various imaging applications.

Contribution

It offers a systematic analysis of DCS hyperparameters using brute-force search, which is a novel approach for optimizing signal recovery.

Findings

Hyperparameters significantly influence DCS reconstruction quality.

Guidelines for setting hyperparameters improve signal recovery performance.

Brute-force search effectively identifies optimal hyperparameter configurations.

Abstract

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.