We propose PISE, a physics-informed deep ghost imaging framework for low-bandwidth edge perception. By combining adjoint operator initialization with semantic guidance, PISE improves classification accuracy by 2.57% and reduces variance by 9x at 5% sampling.
Cite
@article{arxiv.2601.12551,
title = {PISE: Physics-Anchored Semantically-Enhanced Deep Computational Ghost Imaging for Robust Low-Bandwidth Machine Perception},
author = {Tong Wu},
journal= {arXiv preprint arXiv:2601.12551},
year = {2026}
}
Comments
4 pages, 4 figures, 4 tables. Refined version with updated references and formatting improvements