An MEM-DMD-Enabled Ghost Imaging System Enhanced by a Hybrid CNN-GAN for High-Resolution Imaging Under Scattering Media

This paper presents a Micro-Electro-Mechanical Systems digital micromirror device (MEMS-DMD)-enabled ghost imaging (GI) framework for high-resolution imaging under scattering conditions. Unlike conventional ghost imaging systems that rely on fixed illumination patterns, the proposed approach exploits the high-speed programmability of a DMD to implement adaptive illumination strategies, enabling dynamic selection of informative patterns during data acquisition. This hardware-enabled pattern selection strategy improves sampling efficiency and reconstruction stability under the modeled fog conditions considered here. A hybrid convolutional neural network–generative adversarial network (CNN–GAN) model is employed as an inversion tool to reconstruct high-quality images from compressed bucket measurements. The proposed system achieves substantial improvements in reconstruction quality, with 23–40% gains in PSNR and 18–26% in SSIM compared to traditional ghost imaging methods, while reducing the number of required measurements by up to 60%. Additional performance gains are achieved through adaptive pattern selection enabled by the MEMS-DMD. The results demonstrate that integrating programmable MEMS hardware with learning-based reconstruction provides an effective solution for imaging under scattering conditions, with potential applications in remote sensing, environmental monitoring, and surveillance.