Noise2Ghost: Self-supervised deep convolutional reconstruction for ghost imaging

Mathieu Manni; Dmitry Karpov; K. Joost Batenburg; Sharon Shwartz; Nicola Viganò

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arXiv · 2504.10288

Noise2Ghost: Self-supervised deep convolutional reconstruction for ghost imaging

Mathieu Manni·Dmitry Karpov·K. Joost Batenburg·Sharon Shwartz·Nicola Viganò

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Abstract

We present a new self-supervised deep-learning-based Ghost Imaging (GI) reconstruction method, which provides unparalleled reconstruction quality for noisy acquisitions among unsupervised methods. We present the supporting mathematical framework and results from theoretical and real data use cases. Self-supervision removes the need for clean reference data while offering strong noise reduction. This provides the necessary tools for addressing signal-to-noise ratio concerns for GI acquisitions in emerging and cutting-edge low-light GI scenarios. Notable examples include micro- and nano-scale x-ray emission imaging, e.g., x-ray fluorescence imaging of dose-sensitive samples. Their applications include in-vivo and in-operando case studies for biological samples and batteries.

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BibTeX RIS

Mathieu Manni, Dmitry Karpov, K. Joost Batenburg, Sharon Shwartz, Nicola Viganò. 2025-04-14. Noise2Ghost: Self-supervised deep convolutional reconstruction for ghost imaging. https://arxiv.org/abs/2504.10288

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