no code implementations • 28 Feb 2024 • Chu Zhou, Minggui Teng, Xinyu Zhou, Chao Xu, Boxin Sh
However, since the on-chip micro-polarizers block part of the light so that the sensor often requires a longer exposure time, the captured polarized images are prone to motion blur caused by camera shakes, leading to noticeable degradation in the computed DoP and AoP.
no code implementations • CVPR 2024 • Xinyu Zhou, Peiqi Duan, Boyu Li, Chu Zhou, Chao Xu, Boxin Shi
In this paper we leverage the event camera to facilitate the separation of direct and global components enabling video-rate separation of high quality.
no code implementations • CVPR 2024 • Yifei Xia, Chu Zhou, Chengxuan Zhu, Minggui Teng, Chao Xu, Boxin Shi
The removal of atmospheric turbulence is crucial for long-distance imaging.
no code implementations • CVPR 2023 • Yakun Chang, Chu Zhou, Yuchen Hong, Liwen Hu, Chao Xu, Tiejun Huang, Boxin Shi
Capturing high frame rate and high dynamic range (HFR&HDR) color videos in high-speed scenes with conventional frame-based cameras is very challenging.
no code implementations • NeurIPS 2021 • Chu Zhou, Minggui Teng, Yufei Han, Chao Xu, Boxin Shi
Haze, a common kind of bad weather caused by atmospheric scattering, decreases the visibility of scenes and degenerates the performance of computer vision algorithms.
no code implementations • ICCV 2021 • Jin Han, Yixin Yang, Chu Zhou, Chao Xu, Boxin Shi
To reconstruct high-resolution intensity images from event data, we propose EvIntSR-Net that converts event data to multiple latent intensity frames to achieve super-resolution on intensity images in this paper.
no code implementations • NeurIPS 2020 • Chu Zhou, Hang Zhao, Jin Han, Chang Xu, Chao Xu, Tiejun Huang, Boxin Shi
A conventional camera often suffers from over- or under-exposure when recording a real-world scene with a very high dynamic range (HDR).
no code implementations • 4 Oct 2019 • Urmish Thakker, Igor Fedorov, Jesse Beu, Dibakar Gope, Chu Zhou, Ganesh Dasika, Matthew Mattina
This paper introduces a method to compress RNNs for resource constrained environments using Kronecker product (KP).
no code implementations • 7 Jun 2019 • Urmish Thakker, Jesse Beu, Dibakar Gope, Chu Zhou, Igor Fedorov, Ganesh Dasika, Matthew Mattina
Recurrent Neural Networks (RNN) can be difficult to deploy on resource constrained devices due to their size. As a result, there is a need for compression techniques that can significantly compress RNNs without negatively impacting task accuracy.