Noise-robust latent vector reconstruction in ptychography using deep generative models

Jacob Seifert*, Yifeng Shao, Allard P. Mosk

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Computational imaging is increasingly vital for a broad spectrum of applications, ranging from biological to material sciences. This includes applications where the object is known and sufficiently sparse, allowing it to be described with a reduced number of parameters. When no explicit parameterization is available, a deep generative model can be trained to represent an object in a low-dimensional latent space. In this paper, we harness this dimensionality reduction capability of autoencoders to search for the object solution within the latent space rather than the object space. We demonstrate what we believe to be a novel approach to ptychographic image reconstruction by integrating a deep generative model obtained from a pre-trained autoencoder within an automatic differentiation ptychography (ADP) framework. This approach enables the retrieval of objects from highly ill-posed diffraction patterns, offering an effective method for noise-robust latent vector reconstruction in ptychography. Moreover, the mapping into a low-dimensional latent space allows us to visualize the optimization landscape, which provides insight into the convexity and convergence behavior of the inverse problem. With this work, we aim to facilitate new applications for sparse computational imaging such as when low radiation doses or rapid reconstructions are essential.

Original languageEnglish
Pages (from-to)1020-1033
Number of pages14
JournalOptics Express
Volume32
Issue number1
DOIs
Publication statusPublished - 2024

Funding

Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Perspective P16-08). We thank Dorian Bouchet for helpful discussions and Cees de Kok, Dante Killian, Jan Bonne Aans, Aron Opheij, Paul Jurrius and Arjan Driessen for technical support.

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