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Hyperparameter Analysis for Derivative Compressive Sampling

arXiv 2108.04355 ↗First posted 5 Aug 2021cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 24 references that are in the map. First posted on arXiv 5 Aug 2021.

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3 of 24 references.

References · 3

  1. 2017Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfercited by 19
  2. 2017Joint Dictionaries for Zero-Shot Learningcited by 9
  3. 2016Testing fine-grained parallelism for the ADMM on a factor-graphcited by 3

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