no code implementations • 27 Apr 2024 • Zheng Cheng, Guodong Fan, Jingchun Zhou, Min Gan, C. L. Philip Chen
The FDCE-Net consists of two main structures: (1) Frequency Spatial Network (FS-Net) aims to achieve initial enhancement by utilizing our designed Frequency Spatial Residual Block (FSRB) to decouple image degradation factors in the frequency domain and enhance different attributes separately.
no code implementations • 12 Dec 2023 • Jingchun Zhou, Zongxin He, Qiuping Jiang, Kui Jiang, Xianping Fu, Xuelong Li
To solve this issue, previous methods often idealize the degradation process, and neglect the impact of medium noise and object motion on the distribution of image features, limiting the generalization and adaptability of the model.
no code implementations • 12 Dec 2023 • Jingchun Zhou, Qilin Gai, Kin-Man Lam, Xianping Fu
In underwater environments, variations in suspended particle concentration and turbidity cause severe image degradation, posing significant challenges to image enhancement (IE) and object detection (OD) tasks.
no code implementations • 12 Dec 2023 • Jingchun Zhou, Tianyu Liang, Dehuan Zhang, Zongxin He
Neural Radiance Field (NeRF) technology demonstrates immense potential in novel viewpoint synthesis tasks, due to its physics-based volumetric rendering process, which is particularly promising in underwater scenes.
1 code implementation • 23 Aug 2023 • Dehuan Zhang, Jingchun Zhou, Chunle Guo, Weishi Zhang, Chongyi Li
Therefore, we present the synergistic multi-scale detail refinement via intrinsic supervision (SMDR-IS) for enhancing underwater scene details, which contain multi-stages.
1 code implementation • 23 Aug 2023 • Jingchun Zhou, Zongxin He, Kin-Man Lam, Yudong Wang, Weishi Zhang, Chunle Guo, Chongyi Li
In this paper, we present a novel Amplitude-Modulated Stochastic Perturbation and Vortex Convolutional Network, AMSP-UOD, designed for underwater object detection.