1 code implementation • 30 Apr 2024 • Yong Shu, Liquan Shen, Xiangyu Hu, Mengyao Li, ZiHao Zhou
In this work, to facilitate the development of real-world HDR video reconstruction, we present Real-HDRV, a large-scale real-world benchmark dataset for HDR video reconstruction, featuring various scenes, diverse motion patterns, and high-quality labels.
1 code implementation • 17 Aug 2023 • Mengyao Li, Liquan Shen, Peng Ye, Guorui Feng, Zheyin Wang
Subsequently, an extreme UWI compression network with reference to the feature dictionary (RFD-ECNet) is creatively proposed, which utilizes feature match and reference feature variant to significantly remove redundancy among UWIs.
1 code implementation • 14 Jun 2023 • Zhengyong Wang, Liquan Shen, Yihan Yu, Yuan Hui
With performing region-wise feature learning for regions with different quality separately, the network provides an effective guidance for global features and thus guides intra-image differentiated enhancement.
no code implementations • 4 May 2023 • Xuhao Jiang, Weimin Tan, Qing Lin, Chenxi Ma, Bo Yan, Liquan Shen
In recent years, many convolutional neural network-based models are designed for JPEG artifacts reduction, and have achieved notable progress.
no code implementations • 26 Apr 2023 • Xuhao Jiang, Weimin Tan, Tian Tan, Bo Yan, Liquan Shen
Image-based single-modality compression learning approaches have demonstrated exceptionally powerful encoding and decoding capabilities in the past few years , but suffer from blur and severe semantics loss at extremely low bitrates.
1 code implementation • ICCV 2023 • Mengyao Li, Liquan Shen, Peng Ye, Guorui Feng, Zheyin Wang
Subsequently, an extreme UWI compression network with reference to the feature dictionary (RFD-ECNet) is creatively proposed, which utilizes feature match and reference feature variant to significantly remove redundancy among UWIs.
1 code implementation • 22 Aug 2021 • Zhengyong Wang, Liquan Shen, Mei Yu, Kun Wang, Yufei Lin, Mai Xu
However, these methods ignore the significant domain gap between the synthetic and real data (i. e., interdomain gap), and thus the models trained on synthetic data often fail to generalize well to real underwater scenarios.
no code implementations • 20 Aug 2021 • Zhengyong Wang, Liquan Shen, Mei Yu, Yufei Lin, Qiuyu Zhu
The proposed framework includes an analysis network and a synthesis network, one for priors exploration and another for priors integration.
1 code implementation • journal 2021 • Qing Ding, Liquan Shen, Liangwei Yu, Hao Yang, Mai Xu
To overcome these limitations, we propose a patch-wise spatial-temporal quality enhancement network which firstly extracts spatial and temporal features, then recalibrates and fuses the obtained spatial and temporal features.
no code implementations • 2 Mar 2019 • Xuhao Jiang, Liquan Shen, Guorui Feng, Liangwei Yu, Ping An
In this paper, we propose a novel quadratic optimized model based on the deep convolutional neural network (QODCNN) for full-reference and no-reference screen content image (SCI) quality assessment.