Search Results for author: RuiXing Wang

Found 8 papers, 6 papers with code

Low-Light Image Enhancement via Structure Modeling and Guidance

1 code implementation CVPR 2023 Xiaogang Xu, RuiXing Wang, Jiangbo Lu

Moreover, to improve the appearance modeling, which is implemented with a simple U-Net, a novel structure-guided enhancement module is proposed with structure-guided feature synthesis layers.

Edge Detection Low-Light Image Enhancement

Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super Resolution

1 code implementation5 Jul 2022 Xiaogang Xu, RuiXing Wang, Chi-Wing Fu, Jiaya Jia

Despite the quality improvement brought by the recent methods, video super-resolution (SR) is still very challenging, especially for videos that are low-light and noisy.

Denoising Video Denoising +1

SNR-Aware Low-Light Image Enhancement

1 code implementation CVPR 2022 Xiaogang Xu, RuiXing Wang, Chi-Wing Fu, Jiaya Jia

They are long-range operations for image regions of extremely low Signal-to-Noise-Ratio (SNR) and short-range operations for other regions.

Low-Light Image Enhancement

SERank: Optimize Sequencewise Learning to Rank Using Squeeze-and-Excitation Network

1 code implementation7 Jun 2020 RuiXing Wang, Kuan Fang, RiKang Zhou, Zhan Shen, LiWen Fan

Recently, there are a few methods have been proposed which focused on mining information across ranking candidates list for further improvements, such as learning multivariant scoring function or learning contextual embedding.

Learning-To-Rank Question Answering

Underexposed Photo Enhancement Using Deep Illumination Estimation

1 code implementation CVPR 2019 Ruixing Wang, Qing Zhang, Chi-Wing Fu, Xiaoyong Shen, Wei-Shi Zheng, Jiaya Jia

Based on this model, we formulate a loss function that adopts constraints and priors on the illumination, prepare a new dataset of 3, 000 underexposed image pairs, and train the network to effectively learn a rich variety of adjustment for diverse lighting conditions.

Automatic Real-time Background Cut for Portrait Videos

no code implementations28 Apr 2017 Xiaoyong Shen, RuiXing Wang, Hengshuang Zhao, Jiaya Jia

A spatial-temporal refinement network is developed to further refine the segmentation errors in each frame and ensure temporal coherence in the segmentation map.

Segmentation Semantic Segmentation +2

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