arXiv subjects

Benedikt Diederichs

Publications and source records attributed to Benedikt Diederichs.

5 recordsLinked to original sources

arXiv2024

Wirtinger gradient descent methods for low-dose Poisson phase retrieval

The problem of phase retrieval has many applications in the field of optical imaging. Motivated by imaging experiments with biological specimens, we primarily consider the setting of low-dose illumination where Poisson noise plays the dominant role. In this paper, we discuss gradient descent algorithms based on different loss functions adapted to data affected by Poisson noise, in particular in the low-dose regime. Starting from the maximum log-likelihood function for the Poisson distribution, we investigate different regularizations and approximations of the problem to design an algorithm that meets the requirements that are faced in applications. In the course of this, we focus on low-count measurements. For all suggested loss functions, we study the convergence of the respective gradient descent algorithms to stationary points and find constant step sizes that guarantee descent of the loss in each iteration. Numerical experiments in the low-dose regime are performed to corroborate the theoretical observations.

math.NA↗

arXiv2022

How many Fourier coefficients are needed?

We are looking at families of functions or measures on the torus which are specified by a finite number of parameters $N$. The task, for a given family, is to look at a small number of Fourier coefficients of the object, at a set of locations that is predetermined and may depend only on $N$, and determine the object. We look at (a) the indicator functions of at most $N$ intervals of the torus and (b) at sums of at most $N$ complex point masses on the multidimensional torus. In the first case we reprove a theorem of Courtney which says that the Fourier coefficients at the locations $0, 1, \ldots, N$ are sufficient to determine the function (the intervals). In the second case we produce a set of locations of size $O(N \log^{d-1} N)$ which suffices to determine the measure.

math.CA↗

arXiv2022

Inverse Multislice Ptychography by Layer-wise Optimisation and Sparse Matrix Decomposition

We propose algorithms based on an optimisation method for inverse multislice ptychography in, e.g. electron microscopy. The multislice method is widely used to model the interaction between relativistic electrons and thick specimens. Since only the intensity of diffraction patterns can be recorded, the challenge in applying inverse multislice ptychography is to uniquely reconstruct the electrostatic potential in each slice up to some ambiguities. In this conceptual study, we show that a unique separation of atomic layers for simulated data is possible when considering a low acceleration voltage. We also introduce an adaptation for estimating the illuminating probe. For the sake of practical application, we finally present slice reconstructions using experimental 4D scanning transmission electron microscopy (STEM) data.

cs.IT↗

arXiv2020

The spectral properties of Vandermonde matrices with clustered nodes

We study rectangular Vandermonde matrices $\mathbf{V}$ with $N+1$ rows and $s$ irregularly spaced nodes on the unit circle, in cases where some of the nodes are "clustered" together -- the elements inside each cluster being separated by at most $h \lesssim {1\over N}$, and the clusters being separated from each other by at least $θ\gtrsim {1\over N}$. We show that any pair of column subspaces corresponding to two different clusters are nearly orthogonal: the minimal principal angle between them is at most $$\fracπ{2}-\frac{c_1}{N θ}-c_2 N h,$$ for some constants $c_1,c_2$ depending only on the multiplicities of theclusters. As a result, spectral analysis of $\mathbf{V}_N$ is significantly simplified by reducing the problem to the analysis of each cluster individually. Consequently we derive accurate estimates for 1) all the singular values of $\mathbf{V}$, and 2) componentwise condition numbers for the linear least squares problem. Importantly, these estimates are exponential only in the local cluster multiplicities, while changing at most linearly with $s$.

math.NA↗

arXiv2019

Well-Posedness of Sparse Frequency Estimation

The problem of estimating the frequencies of an exponential sum has been studied extensively over the last years. It can be understood as a sparse estimation problem, as it strives to identify the sparse representation of a signal using exponentials. In this paper, we are interested in its intrinsic stability properties. We derive a bound very similar to the restricted isometry property. Conditional well-posedness follows: Any exponential sum with samples close to the unknown ground truth has close frequencies as well, provided that it satisfies our model assumptions. The most important assumption is that the frequencies are well-separated. Furthermore, we show that the presented bound is sharp and gives rise to improved estimates of condition numbers of certain Vandermonde matrices.

math.NA↗