Self-supervised deep convolutional reconstruction for ghost imaging.
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Updated
Jul 28, 2026 - Python
Self-supervised deep convolutional reconstruction for ghost imaging.
This repository introduces Generalized SHSI using tunable sparse Hadamard speckle patterns to achieve noise‑robust, high‑resolution imaging with low‑order Hadamard matrices while alleviating the curse of dimensionality.
Few codes with the tools needed for doing Ghost Imaging simulations: speckle generation, simulation of measurements, recovery, etc.
This repository presents loop differential ghost imaging with line patterns generated by a 1D OPA with a grating waveguide, revealing a limitation of conventional ghost imaging caused by noise‑symmetry breaking under such illumination.
Image-free segmentation straight from single-pixel measurements — content-adaptive lift + task-prioritized loss scheduling (PyTorch)
Ten deep-learning single-pixel image reconstruction methods benchmarked on one shared Hadamard acquisition (PyTorch)
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