Zhihong Zhang

I am a Research Engineer specializing in computational photography, computer vision, and machine learning.

I received my Ph.D. in Control Science and Engineering from Tsinghua University in 2024, advised by Prof. Jinli Suo. My doctoral research focused on developing integrated sensing-processing-task frameworks. Specifically, I explored the co-design of front-end imaging systems and backend vision algorithms to enhance performance across both low-level and high-level vision tasks. Prior to my Ph.D., I earned my B.Eng. in Electronics and Information Engineering from Xidian University in 2019.

Currently, my work revolves around computational photography and large vision models. I also maintain a strong interest in emerging AI technologies, including vision-language models, world models, and robotics.

Recent News

Sep 5, 2024	Our review paper “A Decade Review of Video Compressive Sensing: A Roadmap to Practical Applications” is accepted by *Engineering*
Sep 4, 2024	Our paper “Lightweight High-Speed Photography Built on Coded Exposure and Implicit Neural Representation of Videos” is accepted by *Int. J. Comput. Vis.*
Jul 28, 2023	Our paper “Deep Coded Exposure: End-to-End Co-optimization of Flutter Shutter and Deblurring Processing for General Motion Blur Removal” is accepted by *Photonics Res.*
Feb 8, 2023	Our paper “INFWIDE: Image and Feature Space Wiener Deconvolution Network for Non-blind Image Deblurring in Low-Light Conditions” is accepted by *IEEE Trans. Image Process.*.
Jan 1, 2023	Our paper “A mutually boosting dual sensor computational camera for high quality dark videography” is accepted by *Information Fusion*.

Selected Publications

IJCV
Lightweight High-Speed Photography Built on Coded Exposure and Implicit Neural Representation of Videos

Zhihong Zhang, Runzhao Yang, Jinli Suo*, Yuxiao Cheng, and Qionghai Dai

International Journal of Computer Vision, 2024

Abstract Bib HTML Code

By leveraging classical coded exposure imaging technique and emerging implicit neural representation for videos, we develop a novel self-recursive neural network to sequentially retrieve the latent video sequence from the blurry image utilizing the embedded motion direction cues.
@article{zhangBDINR2024, title = {Lightweight {{High-Speed Photography Built}} on {{Coded Exposure}} and {{Implicit Neural Representation}} of {{Videos}}}, author = {Zhang, Zhihong and Yang, Runzhao and Suo*, Jinli and Cheng, Yuxiao and Dai, Qionghai}, year = {2024}, journal = {International Journal of Computer Vision}, issn = {0920-5691, 1573-1405}, doi = {10.1007/s11263-024-02198-1}, }
Engineering
A Decade Review of Video Compressive Sensing: A Roadmap to Practical Applications

Zhihong Zhangᐪ, Siming Zhengᐪ, Min Qiu, Guohai Situ, David J. Brady, and 3 more authors

Engineering, 2024

Abstract Bib HTML

This paper provides a review of the advancements in video compressive sensing over the past decade. Research gaps and future directions towards real-world applications are put forward as well.
@article{ZHANG2024SCI, title = {A Decade Review of Video Compressive Sensing: A Roadmap to Practical Applications}, journal = {Engineering}, author = {Zhangᐪ, Zhihong and Zhengᐪ, Siming and Qiu, Min and Situ, Guohai and Brady, David J. and Dai, Qionghai and Suo*, Jinli and Yuan*, Xin}, year = {2024}, issn = {2095-8099}, doi = {https://doi.org/10.1016/j.eng.2024.08.013}, }
Photonics Res.
Deep Coded Exposure: End-to-End Co-optimization of Flutter Shutter and Deblurring Processing for General Motion Blur Removal

Zhihong Zhang, Kaiming Dong, Jinli Suo*, and Qionghai Dai

Photonics Research, 2023

Abstract Bib HTML Code

We proposed an end-to-end framework to handle general motion blurs with a unified deep neural network, and optimize the shutter’s encoding pattern together with the deblurring processing to achieve high-quality sharp images.
title = {Deep Coded Exposure: End-to-End Co-optimization of Flutter Shutter and Deblurring Processing for General Motion Blur Removal}, author = {Zhang, Zhihong and Dong, Kaiming and Suo*, Jinli and Dai, Qionghai}, journal = {Photonics Research}, year = {2023}, volume = {11}, number = {10}, pages = {1678}, doi = {10.1364/PRJ.489989}, }
IEEE TIP
INFWIDE: Image and Feature Space Wiener Deconvolution Network for Non-blind Image Deblurring in Low-Light Conditions

Zhihong Zhang, Yuxiao Cheng, Jinli Suo*, Liheng Bian, and Qionghai Dai

IEEE Transactions on Image Processing, 2023

Abstract Bib HTML Code

Under low-light environment, handheld photography suffers from severe camera shake under long exposure settings. Sophisticated noise and saturation regions are two dominating challenges in practical low-light deblurring. In this work, we propose a novel non-blind deblurring method dubbed image and feature space Wiener deconvolution network (INFWIDE) to tackle these problems systematically.
@article{infwide_zhihong, title = {{{INFWIDE}}: {{Image}} and Feature Space Wiener Deconvolution Network for Non-blind Image Deblurring in Low-Light Conditions}, author = {Zhang, Zhihong and Cheng, Yuxiao and Suo*, Jinli and Bian, Liheng and Dai, Qionghai}, journal = {IEEE Transactions on Image Processing}, year = {2023}, volume = {32}, pages = {1390--1402}, doi = {10.1109/TIP.2023.3244417}, }

PhotoniX

From Compressive Sampling to Compressive Tasking: Retrieving Semantics in Compressed Domain with Low Bandwidth

Zhihong Zhangᐪ, Bo Zhangᐪ, Xin Yuanᐪ, Siming Zheng, Xiongfei Su, and 3 more authors

PhotoniX, 2022

Abstract Bib HTML

@article{zhang2022CompressiveSampling,
  title = {From Compressive Sampling to Compressive Tasking: Retrieving Semantics in Compressed Domain with Low Bandwidth},
  shorttitle = {From Compressive Sampling to Compressive Tasking},
  author = {Zhangᐪ, Zhihong and Zhangᐪ, Bo and Yuanᐪ, Xin and Zheng, Siming and Su, Xiongfei and Suo*, Jinli and Brady, David J. and Dai*, Qionghai},
  year = {2022},
  journal = {PhotoniX},
  volume = {3},
  number = {1},
  pages = {19},
}

Photonics Asia

Denoising of Event-Based Sensors with Deep Neural Networks

Zhihong Zhang, Jinli Suo*, and Qionghai Dai

In Optoelectron. Imaging Multimed. Technol. VIII, 2021

Abstract Bib HTML Code Poster

@inproceedings{Event_denoise,
  title = {Denoising of Event-Based Sensors with Deep Neural Networks},
  booktitle = {Optoelectron. {{Imaging Multimed}}. {{Technol}}. {{VIII}}},
  author = {Zhang, Zhihong and Suo*, Jinli and Dai, Qionghai},
  year = {2021},
  volume = {11897},
  pages = {203--209},
  publisher = {{SPIE}},
  organization = {{International Society for Optics and Photonics}},
}

Photonics Res.
Ten-Mega-Pixel Snapshot Compressive Imaging with a Hybrid Coded Aperture

Zhihong Zhangᐪ, Chao Dengᐪ, Yang Liu, Xin Yuan, Jinli Suo*, and 1 more author

Photonics Research, 2021

Abstract Bib HTML Code

A 10-mega-pixel high-throughput high-speed snapshot compressive imaging (SCI) system with a hybrid coded aperture.
@article{zhang2021TenmegapixelSnapshot, title = {Ten-Mega-Pixel Snapshot Compressive Imaging with a Hybrid Coded Aperture}, author = {Zhangᐪ, Zhihong and Dengᐪ, Chao and Liu, Yang and Yuan, Xin and Suo*, Jinli and Dai, Qionghai}, year = {2021}, journal = {Photonics Research}, volume = {9}, number = {11}, pages = {2277--2287}, }

Academic Service

Reviewer:	IEEE TCSVT , Photonics Res. , Opt. Express , Opt. Letters , JOSA A