PISE: Physics-Anchored Semantically-Enhanced Deep Computational Ghost Imaging for Robust Low-Bandwidth Machine Perception
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 23 references that are in the map. First posted on arXiv 18 Jan 2026.
Builds on
5 of 23 references.
References · 5
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
- 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,694
- 2018On the Spectral Bias of Neural Networkscited by 1,341
- 2020AMP-Net: Denoising based Deep Unfolding for Compressive Image Sensingcited by 68
- 2017Experimental comparison of single-pixel imaging algorithmscited by 5
Built on by
Not cited yet.