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arXiv · 2607.26216

DO-CGI: deep-optimized illumination patterns for computational ghost imaging at low sampling ratios

Mor Hale·Ofir Lindenbaum·Eliahu Cohen
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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.

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Mor Hale, Ofir Lindenbaum, Eliahu Cohen. 2026-07-28. DO-CGI: deep-optimized illumination patterns for computational ghost imaging at low sampling ratios. https://arxiv.org/abs/2607.26216

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