no code implementations • 25 Oct 2024 • Zhenming Yu, Liming Cheng, Hongyu Huang, Wei zhang, Liang Lin, Kun Xu
Herein, we propose a novel framework that integrates communication and computational imaging (ICCI) to break through the inherent isolation between communication and computational imaging for remote perception.
no code implementations • 18 Sep 2024 • Hongyu Huang, Zhenming Yu, Yi Lei, Wei zhang, Yongli Zhao, Shanguo Huang, Kun Xu
To effectively mitigate the influence of atmospheric turbulence, a novel discrete-time analog transmission free-space optical (DTAT-FSO) communication scheme is proposed.
no code implementations • 27 Dec 2022 • Zhenming Yu, Hongyu Huang, Liming Cheng, Wei zhang, Yueqiu Mu, Kun Xu
The current optical communication systems minimize bit or symbol errors without considering the semantic meaning behind digital bits, thus transmitting a lot of unnecessary information.
1 code implementation • ICCV 2021 • Ziyi Meng, Zhenming Yu, Kun Xu, Xin Yuan
In this paper, inspired by the untrained neural networks such as deep image priors (DIP) and deep decoders, we develop a framework by integrating DIP into the plug-and-play regime, leading to a self-supervised network for spectral SCI reconstruction.
no code implementations • 24 Aug 2021 • Yubin Zang, Zhenming Yu, Kun Xu, Xingzeng Lan, Minghua Chen, Sigang Yang, Hongwei Chen
Instead of adopting input signals and output signals which are calculated by SSFM algorithm in advance before training, this principle-driven PINN based fiber model adopts frames of time and distance as its inputs and the corresponding real and imaginary parts of NLSE solutions as its outputs.
no code implementations • 15 Jun 2016 • Qiang Guo, Hongwei Chen, Yuxi Wang, Yong Guo, Peng Liu, Xiurui Zhu, Zheng Cheng, Zhenming Yu, Minghua Chen, Sigang Yang, Shizhong Xie
However, according to CS theory, image reconstruction is an iterative process that consumes enormous amounts of computational time and cannot be performed in real time.