activity
20172020
most citedA Deep Learning Approach for Blind Drift Calibration of Sensor Networks

91 citations · 93 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV2020

NBNet: Noise Basis Learning for Image Denoising with Subspace Projection

Shen Cheng, Yuzhi Wang, Haibin Huang +3

In this paper, we introduce NBNet, a novel framework for image denoising. Unlike previous works, we propose to tackle this challenging problem from a new perspective: noise reducti…

eess.IV2020

Practical Deep Raw Image Denoising on Mobile Devices

Yuzhi Wang, Haibin Huang, Qin Xu +3

Deep learning-based image denoising approaches have been extensively studied in recent years, prevailing in many public benchmark datasets. However, the stat-of-the-art networks ar…

cs.CV2019

Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation

Jiaming Liu, Chi-Hao Wu, Yuzhi Wang +8

In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising. Compared with traditional RGB image denoising, performing this task…

cs.CV20172 cited

Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks

Shuchang Zhou, Yuzhi Wang, He Wen +2

Quantized Neural Networks (QNNs), which use low bitwidth numbers for representing parameters and performing computations, have been proposed to reduce the computation complexity, s…

cs.LG201791 cited

A Deep Learning Approach for Blind Drift Calibration of Sensor Networks

Yuzhi Wang, Anqi Yang, Xiaoming Chen +3

Temporal drift of sensory data is a severe problem impacting the data quality of wireless sensor networks (WSNs). With the proliferation of large-scale and long-term WSNs, it is be…