most citedSelf-supervised Image Enhancement Network: Training with Low Light Images Only

93 citations · 119 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV20214 cited

Learning the Superpixel in a Non-iterative and Lifelong Manner

Lei Zhu, Qi She, Bin Zhang +4

Superpixel is generated by automatically clustering pixels in an image into hundreds of compact partitions, which is widely used to perceive the object contours for its excellent c…

cs.CV202118 cited

Self-supervised Low Light Image Enhancement and Denoising

Yu Zhang, Xiaoguang Di, Bin Zhang +3

This paper proposes a self-supervised low light image enhancement method based on deep learning, which can improve the image contrast and reduce noise at the same time to avoid the…

cs.CV2020

Robust Two-Stream Multi-Feature Network for Driver Drowsiness Detection

Qi Shen, Shengjie Zhao, Rongqing Zhang +1

Drowsiness driving is a major cause of traffic accidents and thus numerous previous researches have focused on driver drowsiness detection. Many drive relevant factors have been ta…

eess.AS2020

Phase-aware music super-resolution using generative adversarial networks

Shichao Hu, Bin Zhang, Beici Liang +2

Audio super-resolution is a challenging task of recovering the missing high-resolution features from a low-resolution signal. To address this, generative adversarial networks (GAN)…

cs.CV20203 cited

Towards Adaptive Semantic Segmentation by Progressive Feature Refinement

Bin Zhang, Shengjie Zhao, Rongqing Zhang

As one of the fundamental tasks in computer vision, semantic segmentation plays an important role in real world applications. Although numerous deep learning models have made notab…

eess.IV20201 cited

Better Than Reference In Low Light Image Enhancement: Conditional Re-Enhancement Networks

Yu Zhang, Xiaoguang Di, Bin Zhang +2

Low light images suffer from severe noise, low brightness, low contrast, etc. In previous researches, many image enhancement methods have been proposed, but few methods can deal wi…