Noise2Ghost: self-supervised deep convolutional reconstruction for ghost imaging

We present a self-supervised deep-learning-based ghost imaging (GI) reconstruction method, which provides a significant improvement in reconstruction quality for noisy acquisitions among unsupervised methods. We present the supporting mathematical framework and results from both theoretical and real data (from a recent synchrotron X-ray GI experiment). 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.