A one-bit quantization approach for low-dose Poisson phase retrieval

Imaging quality for biological tissue is commonly affected by damages of the specimen caused by illumination particles. To mitigate this issue, often very low doses of illumination have to be used in the experiment. Consequently, the resulting inverse problem is subject to highly noisy data. In this note, we address this issue for the case of diffraction imaging by studying the problem of phase retrieval with low-count Poisson data. Our key idea is to exploit the close connection between the Poisson measurement model and the one-bit quantization problem. We propose a reconstruction method based on algorithmic approaches to that problem and compare the performance of this method with state-of-the-art algorithms for noisy phase retrieval, observing superior performance in a number of relevant examples.

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Publication Details

Published
2024-09-18
DOI
https://doi.org/10.1109/cosera60846.2024.10720330
Citations
2
Primary Topic
Advanced X-ray Imaging Techniques
Type
article
Field-Weighted Citation Impact
1.22
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article

A one-bit quantization approach for low-dose Poisson phase retrieval

Patricia Römer, Felix Krahmer
2 citations
Advanced X-ray Imaging Techniques
1.22
article

A one-bit quantization approach for low-dose Poisson phase retrieval

Patricia Römer, Felix Krahmer
article en
2 citations

Abstract

Imaging quality for biological tissue is commonly affected by damages of the specimen caused by illumination particles. To mitigate this issue, often very low doses of illumination have to be used in the experiment. Consequently, the resulting inverse problem is subject to highly noisy data. In this note, we address this issue for the case of diffraction imaging by studying the problem of phase retrieval with low-count Poisson data. Our key idea is to exploit the close connection between the Poisson measurement model and the one-bit quantization problem. We propose a reconstruction method based on algorithmic approaches to that problem and compare the performance of this method with state-of-the-art algorithms for noisy phase retrieval, observing superior performance in a number of relevant examples.

Technical University of Munich (DE)
Openalex Percentile: Top 20%
Advanced X-ray Imaging Techniques
1.22
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A one-bit quantization approach for low-dose Poisson phase retrieval — Patricia Römer, Felix Krahmer · (2024) | TGRS Research Map | TGRS