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20122022
most citedImage denoising with multi-layer perceptrons, part 1: comparison with existing algorithms and with bounds

58 citations · 107 across the 9 of their papers we have counts for

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eess.IV2021

A Closer Look at Reference Learning for Fourier Phase Retrieval

Tobias Uelwer, Nick Rucks, Stefan Harmeling

Reconstructing images from their Fourier magnitude measurements is a problem that often arises in different research areas. This process is also referred to as phase retrieval. In…

eess.IV2021

Non-Iterative Phase Retrieval With Cascaded Neural Networks

Tobias Uelwer, Tobias Hoffmann, Stefan Harmeling

Fourier phase retrieval is the problem of reconstructing a signal given only the magnitude of its Fourier transformation. Optimization-based approaches, like the well-established G…

eess.IV2020

Convolutional Neural Networks for cytoarchitectonic brain mapping at large scale

Christian Schiffer, Hannah Spitzer, Kai Kiwitz +6

Human brain atlases provide spatial reference systems for data characterizing brain organization at different levels, coming from different brains. Cytoarchitecture is a basic prin…

eess.IV2020

Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections

Christian Schiffer, Katrin Amunts, Stefan Harmeling +1

Cytoarchitectonic maps provide microstructural reference parcellations of the brain, describing its organization in terms of the spatial arrangement of neuronal cell bodies as meas…

eess.IV2019

Phase Retrieval Using Conditional Generative Adversarial Networks

Tobias Uelwer, Alexander Oberstraß, Stefan Harmeling

In this paper, we propose the application of conditional generative adversarial networks to solve various phase retrieval problems. We show that including knowledge of the measurem…