activity
20172022
most citedAn All-in-One Network for Dehazing and Beyond

137 citations · 168 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SD20222 cited

End-to-End Neural Speech Coding for Real-Time Communications

Xue Jiang, Xiulian Peng, Chengyu Zheng +3

Deep-learning based methods have shown their advantages in audio coding over traditional ones but limited attention has been paid on real-time communications (RTC). This paper prop…

eess.AS20211 cited

Phoneme-based Distribution Regularization for Speech Enhancement

Yajing Liu, Xiulian Peng, Zhiwei Xiong +1

Existing speech enhancement methods mainly separate speech from noises at the signal level or in the time-frequency domain. They seldom pay attention to the semantic information of…

eess.AS20204 cited

Interactive Speech and Noise Modeling for Speech Enhancement

Chengyu Zheng, Xiulian Peng, Yuan Zhang +2

Speech enhancement is challenging because of the diversity of background noise types. Most of the existing methods are focused on modelling the speech rather than the noise. In thi…

cs.CV201724 cited

End-to-End United Video Dehazing and Detection

Boyi Li, Xiulian Peng, Zhangyang Wang +2

The recent development of CNN-based image dehazing has revealed the effectiveness of end-to-end modeling. However, extending the idea to end-to-end video dehazing has not been expl…

cs.CV2017137 cited

An All-in-One Network for Dehazing and Beyond

Boyi Li, Xiulian Peng, Zhangyang Wang +2

This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net). It is designed based on a re-formulated a…