Single-pixel image reconstruction using coherent nonlinear optics

Matthew Thomas, Santosh Kumar, Yu Ping Huang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

We propose and experimentally demonstrate a novel, to the best of our knowledge, hybrid optoelectronic system that utilizes mode-selective frequency upconversion, single-pixel detection, and a deep neural network to achieve the reliable reconstruction of two-dimensional (2D) images from a noise-contaminated database of handwritten digits. Our system is designed to maximize the multi-scale structural similarity index measure (MS-SSIM) and minimize the mean absolute error (MAE) during the training process. Through extensive evaluation, we have observed that the reconstructed images exhibit high-quality results, with a peak signal-to-noise ratio (PSNR) reaching approximately 20 dB and a structural similarity index measure (SSIM) of around 0.85. These impressive metrics demonstrate the effectiveness and fidelity of our image reconstruction technique. The versatility of our approach allows its application in various fields, including Lidar, compressive imaging, volumetric reconstruction, and so on.

Original languageEnglish
Pages (from-to)4320-4323
Number of pages4
JournalOptics Letters
Volume48
Issue number16
DOIs
StatePublished - 2023

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