@inproceedings{19fae8378914425e90f48938ad3adf11,
title = "Towards high-resolution undersampled single-pixel imaging: A neural network perspective",
abstract = "This paper presents a novel single-pixel imaging (SPI) framework which can produce high-resolution target images with undersampling. Undersampling is used to work around the problem of long imaging time in SPI for real-time applications. However, the reconstruction from undersampled measurements suffers from noise, ringing or pixelated artifacts, and low resolution which complicates target recognition. To improve image quality, deep learning (DL) based approaches have been proposed but the improvement is merely based on noise and artifact removal. In order to improve image resolution, it is necessary to recover fine details from undersampled input which is very challenging due to absence of high-frequency information (during target reconstruction). To achieve this task, we propose to apply a DL model which learns to generate both low and high-frequency representations from an undersampled (10%) 96*96 input, and combines them to produce a high-quality (high-resolution) output. Experimental results show that the proposed model is robust against noise and frequency-based artifacts, and reconstructs high-quality targets by improving resolution (fine details).",
keywords = "Single-pixel imaging, deep learning, denoising, deringing, high-resolution imaging, real-time imaging",
author = "Saad Rizvi and Jie Cao and Qun Hao and Yang Cheng",
note = "Publisher Copyright: {\textcopyright} 2020 SPIE.; 2020 Applied Optics and Photonics China: Optical Sensing and Imaging Technology, AOPC 2020 ; Conference date: 25-08-2020 Through 27-08-2020",
year = "2020",
doi = "10.1117/12.2581412",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Xiangang Luo and Yadong Jiang and Jin Lu and Dong Liu",
booktitle = "AOPC 2020",
address = "United States",
}