Projects funded by the NCN
[inPHASE] Inference algorithms for fringe pattern based quantitative phase imaging
2021/41/N/ST7/04057
Keywords:
inference algorithms deep learning convolutional neural networks interference microscopy computational imaging Bayesian inference
Descriptors:
- ST7_011:
- ST7_005:
Panel:
ST7 - Systems and communication engineering: electronics, communication, optoelectronics
Host institution :
Politechnika Warszawska, Wydział Mechatroniki
woj. mazowieckie
Principal investigator (from the host institution):
Number of co-investigators in the project: 2
Call: PRELUDIUM 20 - announced on 2021-03-15
Amount awarded: 139 568 PLN
Project start date (Y-m-d): 2022-01-04
Project end date (Y-m-d): 2025-01-03
Project duration:: 36 months (the same as in the proposal)
Project status: Project settled
Project description
Download the project description in a pdf file
Note - project descriptions were prepared by the authors of the applications themselves and placed in the system in an unchanged form.
Information in the final report
- Publication in academic press/journals (2)
- DeepVID: deep-learning accelerated variational image decomposition model tailored to fringe pattern filtrationAuthors:Maria Cywińska, Konstanty Szumigaj, Michał Kołodziej, Krzysztof Patorski, Vicente Mico, Shijie Feng, Chao Zuo, Maciej TrusiakAcademic press:Journal of Optics (), Wydawca: IOP PublishingStatus:PublishedDOI:10.1088/2040-8986/acb3df - link to the publication
- DeepOrientation: convolutional neural network for fringe pattern orientation map estimationAuthors:Maria Cywińska, Mikołaj Rogalski, Filip Brzeski, Krzysztof Patorski, Maciej TrusiakAcademic press:Optics Express (), Wydawca: Optica Publishing GroupStatus:PublishedDOI:10.1364/OE.465094 - link to the publication