Noise2Ghost: Self-supervised deep convolutional reconstruction for ghost imaging
This addresses signal-to-noise ratio concerns for ghost imaging in low-light scenarios like micro- and nano-scale x-ray imaging, benefiting applications in biological and battery studies.
The paper tackles the problem of noisy ghost imaging reconstructions by introducing a self-supervised deep learning method that eliminates the need for clean reference data and provides strong noise reduction, achieving unparalleled performance among unsupervised methods.
We present a new self-supervised deep-learning-based Ghost Imaging (GI) reconstruction method, which provides unparalleled reconstruction performance 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.