1 code implementation • 29 Sep 2023 • Zixuan Chen, Zewei He, Ziqian Lu, Xuecheng Sun, Zhe-Ming Lu
We experimentally find that given a dehazing model trained on synthetic data, by fine-tuning the statistics (i. e., mean and standard deviation) of encoding features, PTTD is able to narrow the domain gap, boosting the performance of real image dehazing.
no code implementations • 28 Sep 2023 • Zewei He, Zixuan Chen, Ziqian Lu, Xuecheng Sun, Zhe-Ming Lu
Thus, a multi-receptive-field non-local network (MRFNLN) consisting of the multi-stream feature attention block (MSFAB) and cross non-local block (CNLB) is presented in this paper.
1 code implementation • 12 Jan 2023 • Zixuan Chen, Zewei He, Zhe-Ming Lu
In this paper, a detail-enhanced attention block (DEAB) consisting of the detail-enhanced convolution (DEConv) and the content-guided attention (CGA) is proposed to boost the feature learning for improving the dehazing performance.
Ranked #2 on Image Dehazing on Haze4k
no code implementations • 26 Dec 2020 • Yanlong Cao, Binjie Ding, Zewei He, Jiangxin Yang, Jingxi Chen, Yanpeng Cao, Xin Li
Photometric stereo provides an important method for high-fidelity 3D reconstruction based on multiple intensity images captured under different illumination directions.
no code implementations • 9 Dec 2019 • Du Chen, Zewei He, Yanpeng Cao, Jiangxin Yang, Yanlong Cao, Michael Ying Yang, Siliang Tang, Yueting Zhuang
Firstly, we proposed a novel Orientation-Aware feature extraction and fusion Module (OAM), which contains a mixture of 1D and 2D convolutional kernels (i. e., 5 x 1, 1 x 5, and 3 x 3) for extracting orientation-aware features.