DO-CGI: deep-optimized illumination patterns for computational ghost imaging at low sampling ratios
arXiv:2607.26216
The paper introduces a deep‑learning framework that designs optimized illumination patterns for computational ghost imaging, achieving higher image quality at very low sampling ratios.
Abstract
Computational ghost imaging (CGI) reconstructs objects from known illumination patterns and bucket-detector measurements, but quality deteriorates at low sampling ratios (SRs). We present a deep-learning framework that optimizes grayscale diffuser patterns before reconstruction. In simulations using CIFAR-10 and MNIST images with Split Bregman reconstruction, the learned patterns outperform random patterns in peak signal-to-noise ratio and structural similarity, including at SRs below 5\%. Patterns trained on CIFAR-10 also transfer to MNIST and remain effective under moderate perturbations of the sensing matrix. These results support learned pattern design as a route to fewer CGI measurements.
12 pages, 8 figures, comments are welcome!