Fresnel wavelets for coherent diffractive imaging

(09/2011-06/2019, second and third funding period)

DFG project C11 within the Collaborative Research Center 755 (Nanoscale photonic imaging) Coherent diffractive imaging shows considerable promise to achieve very high spatial resolution. In practice, the most widely applied methods for in-line hologram reconstruction and phase retrieval are iterated projection algorithms. In this project, we aim to construct a new Fresnel wavelet frame that is suitable to represent optimally sparse Fresnel transformed images. We want to apply the new Fresnel wavelet frame for denoising of the measured hologram intensity and to derive new reconstruction schemes for phase retrieval that take the sparsity of the propagated wave field in the Fresnel wavelet frame into account.

Principal investigator: Gerlind Plonka-Hoch
Staff: Robert Beinert, Jakob Geppert, Stefan Loock

Corresponding publications

	One-dimensional discrete-time phase retrieval Robert Beinert, Gerlind Plonka In: Salditt T., Egner A., Luke D. (eds) Nanoscale Photonic Imaging. Topics in Applied Physics, vol 134. Springer, Cham, pp. 603-627, 2020, preprint as download.
	Sparse Power Factorization: Balancing peakiness and sample complexity Dominik Stöger, Jakob Geppert, Felix Krahmer Advances in Computational Mathematics 45(3), 1711--1728, 2019, preprint available at arXiv.
	Sparse Power Factorization With Refined Peakiness Conditions Dominik Stöger, Jakob Geppert, Felix Krahmer 2018 IEEE Statistical Signal Processing Workshop (SSP), 2018, See at IEEE Xplore.
	Enforcing uniqueness in one-dimensional phase retrieval by additional signal information in time domain Robert Beinert, Gerlind Plonka Applied and Computational Harmonic Analysis 45, 505-525, 2018, preprint as download (arXiv).
	Refined performance guarantees for Sparse Power Factorization Jakob Geppert, Felix Krahmer, Dominik Stöger 2017 International Conference on Sampling Theory and Applications (SampTA), 2017, See at IEEE Xplore.
	One-dimensional phase retrieval with additional interference measurements Robert Beinert Results Math. 72(1-2) (2017), 1–24, 2017, preprint as download (arXiv).
	Non-negativity constraints in the one-dimensional discrete-time phase retrieval problem Robert Beinert Information and Inference: A Journal of the IMA 6, 213-224, 2017, preprint as download (arXiv).
	Ambiguities in one-dimensional phase retrieval from magnitudes of a linear canonical transform Robert Beinert Z. Angew. Math. Mech. (ZAMM), 1–5, 2017, preprint as download (arXiv).
	Iterative Phase Retrieval with Sparsity Constraints Stefan Loock, Gerlind Plonka PAMM 16(1), 835-836, 2016, DOI: 10.1002/pamm.201610406.
	Phase Retrieval with Sparsity Constraints Stefan Loock Dissertation, 2016, published online on 29 June 2016.
	Using sparsity information for iterative phase retrieval in x-ray propagation imaging Anne Pein, Stefan Loock, Gerlind Plonka, Tim Salditt Opt. Express 24(8), 8332-8343., 2016, open access.
	Ambiguities in one-dimensional phase retrieval of structured functions Robert Beinert, Gerlind Plonka Proc. Appl. Math. Mech. Volume 15, Issue 1, pp. 653-654, October 2015, DOI: 10.1002/pamm.201510316, preprint as download.
	Ambiguities in one-dimensional discrete phase retrieval from Fourier magnitudes Robert Beinert, Gerlind Plonka The Journal of Fourier Analysis and Applications 21(6), 1169-1198, 2015, preprint as download.
	Phase retrieval for Fresnel measurements using a shearlet sparsity constraint Stefan Loock, Gerlind Plonka Inverse Problems 30 (2014) 055005, 2014, preprint as download.
	How many Fourier samples are needed for real function reconstruction? Gerlind Plonka, Marius Wischerhoff Journal of Applied Mathematics and Computing, Volume 42, Issue 1-2, pp 117-137, 2013, revised preprint as download.

Corresponding software

	pyShearLab - A Python 2D Shearlet Toolbox A Python toolbox for the two-dimensional discrete shearlet transform based on ShearLab3D. Stefan Loock
	PRwSC - A Toolbox for Phase Retrieval with Sparsity Constraints A toolbox for Matlab and Python (pyPRwSC) for the reconstruction of images from phase retrieval data using sparsity constraints. Stefan Loock