Search Results for author: Chu Zhou

Found 5 papers, 0 papers with code

Learning to dehaze with polarization

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.

Image Dehazing Single Image Dehazing

EvIntSR-Net: Event Guided Multiple Latent Frames Reconstruction and Super-Resolution

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.

Super-Resolution

UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging

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).

Pushing the limits of RNN Compression

no code implementations4 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).

Compressing RNNs for IoT devices by 15-38x using Kronecker Products

no code implementations7 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.

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