Articles | Volume 15, issue 19
Research article
14 Oct 2022
Research article |  | 14 Oct 2022

Neural network processing of holographic images

John S. Schreck, Gabrielle Gantos, Matthew Hayman, Aaron Bansemer, and David John Gagne


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-97', Anonymous Referee #2, 23 Jun 2022
    • AC1: 'Response to Reviewer #1', John Schreck, 05 Aug 2022
  • RC2: 'Comment on amt-2022-97', Jan Henneberger, 08 Jul 2022
    • AC2: 'Response to Reviewer #2', John Schreck, 05 Aug 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by John Schreck on behalf of the Authors (05 Aug 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (26 Aug 2022) by Alyn Lambert
RR by Jan Henneberger (02 Sep 2022)
RR by Anonymous Referee #2 (04 Sep 2022)
ED: Publish as is (13 Sep 2022) by Alyn Lambert
AR by John Schreck on behalf of the Authors (19 Sep 2022)  Manuscript 
Short summary
We show promising results for a new machine-learning based paradigm for processing field-acquired cloud droplet holograms. The approach is fast, scalable, and leverages GPUs and other heterogeneous computing platforms. It combines applications of transfer and active learning by using synthetic data for training and a small set of hand-labeled data for refinement and validation. Artificial noise applied during synthetic training enables optimized models for real-world situations.