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.
no code implementations • 12 Mar 2023 • Hao Chen, Zhe-Ming Lu, Jie Liu
This paper focuses on proposing a deep learning-based monkey swing counting algorithm.
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