Title An MEM-DMD-enabled ghost imaging system enhanced by a hybrid CNN-GAN for high-resolution imaging under scattering media
Authors Akhter, Zeenat ; Iqbal, Rehmat ; Janusas, Giedrius ; Urbaite, Sigita ; Palevicius, Arvydas
DOI 10.3390/mi17050598
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Is Part of Micromachines.. Basel : MDPI. 2026, vol. 17, iss. 5, art. no. 598, p. 1-19.. ISSN 2072-666X
Keywords [eng] ghost imaging ; MEMS ; digital micromirror device ; adaptive illumination patterns ; CNN–GAN
Abstract [eng] This paper presents a Micro-Electro-Mechanical Systems digital micromirror device (MEMSDMD)- 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.
Published Basel : MDPI
Type Journal article
Language English
Publication date 2026
CC license CC license description