Jiaquan Yan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Jiaquan Yan has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Antoni Grau
Automatic Control Department, Technical University of Catalonia UPC, 08034 Barcelona, Spain
1 shared publication - Chuansheng Wang
Polytechnic University of Catalonia
1 shared publication Guowei Lei
School of Science, Jimei University, Xiamen, China
1 shared publicationHailan Chen
School of Science, Jimei University, Xiamen, China
1 shared publication- Haiping Xu
Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, School of Computer and Big Data, Minjiang University, Fuzhou City, People’s Republic of China
1 shared publication Hao Hu
Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, School of Computer and Big Data, Minjiang University, Fuzhou, China
1 shared publication
Latest Publications
- Open Access
- 30 August 2025
- Published by Springer Nature in Scientific Reports
- Vol. 15 (1) , 1-12
Mamba-convolution hybrid network for underwater image enhancement
Underwater imagery frequently exhibits low clarity and is subject to significant color distortion as a result of the inherent conditions of the marine environment and variations in illumination. Such degradation in image quality fundamentally undermines the efficacy of marine ecological monitoring and the detection of underwater targets. To address this issue, we present a Mamba-Convolution network for Underwater Image Enhancement (MC-UIE). Concretely, we first use a standard convolution layer with a 3\(\times\)3 kernel to obtain initial image feature maps. Then, we develop an iterable Mamba-Convolution Hybrid Block (M-C HB) to enhance the global and local dependency of image feature maps. The core of the M-C HB is the 2D Selective Scan (SS2D) and Feature Attention Module (FAM), which can more efficiently learn the global and local dependency of images. After that, a Cross Fusion Mamba Block (CFMB) is designed to fuse image feature maps of different levels. Finally, extensive qualitative and quantitative experiments on mainstream datasets demonstrate that the proposed method significantly outperforms existing methods in color, illumination, and detail restoration. Our code and results are available at: https://github.com/WYJGR/MC-Net/.- 30 August 2025
- Published by Springer Nature in Scientific Reports
- Vol. 15 (1) , 1-12
Open AccessMamba-convolution hybrid network for underwater image enhancement
Underwater imagery frequently exhibits low clarity and is subject to significant color distortion as a result of the inherent conditions of the marine environment and variations in illumination. Such degradation in image quality fundamentally undermines the efficacy of marine ecological monitoring and the detection of underwater targets. To address this issue, we present a Mamba-Convolution network for Underwater Image Enhancement (MC-UIE). Concretely, we first use a standard convolution layer with a 3\(\times\)3 kernel to obtain initial image feature maps. Then, we develop an iterable Mamba-Convolution Hybrid Block (M-C HB) to enhance the global and local dependency of image feature maps. The core of the M-C HB is the 2D Selective Scan (SS2D) and Feature Attention Module (FAM), which can more efficiently learn the global and local dependency of images. After that, a Cross Fusion Mamba Block (CFMB) is designed to fuse image feature maps of different levels. Finally, extensive qualitative and quantitative experiments on mainstream datasets demonstrate that the proposed method significantly outperforms existing methods in color, illumination, and detail restoration. Our code and results are available at: https://github.com/WYJGR/MC-Net/. - Open Access
- 8 June 2023
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 11 (6) , 1195
LEPF-Net: Light Enhancement Pixel Fusion Network for Underwater Image Enhancement
Underwater images often suffer from degradation due to scattering and absorption. With the development of artificial intelligence, fully supervised learning-based models have been widely adopted to solve this problem. However, the enhancement performance is susceptible to the quality of the reference images, which is more pronounced in underwater image enhancement tasks because the ground truths are not available. In this paper, we propose a light-enhanced pixel fusion network (LEPF-Net) to solve this problem. Specifically, we first introduce a novel light enhancement block (LEB) based on the residual block (RB) and the light enhancement curve (LE-Curve) to restore the cast color of the images. The RB is adopted to learn and obtain the feature maps from an original input image, and the LE-Curve is used to renovate the color cast of the learned images. To realize the superb detail of the repaired images, which is superior to the reference images, we develop a pixel fusion subnetwork (PF-SubNet) that adopts a pixel attention mechanism (PAM) to eliminate noise from the underwater image. The PAM adapts weight allocation to different levels of a feature map, which leads to an enhancement in the visibility of severely degraded areas. The experimental results show that the proposed LEPF-Net outperforms most of the existing underwater image enhancement methods. Furthermore, among the five classic no-reference image quality assessment (NRIQA) indicators, the enhanced images obtained by LEPF-Net are of higher quality than the ground truths from the UIEB dataset.
- 8 June 2023
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 11 (6) , 1195
Open AccessLEPF-Net: Light Enhancement Pixel Fusion Network for Underwater Image Enhancement
Underwater images often suffer from degradation due to scattering and absorption. With the development of artificial intelligence, fully supervised learning-based models have been widely adopted to solve this problem. However, the enhancement performance is susceptible to the quality of the reference images, which is more pronounced in underwater image enhancement tasks because the ground truths are not available. In this paper, we propose a light-enhanced pixel fusion network (LEPF-Net) to solve this problem. Specifically, we first introduce a novel light enhancement block (LEB) based on the residual block (RB) and the light enhancement curve (LE-Curve) to restore the cast color of the images. The RB is adopted to learn and obtain the feature maps from an original input image, and the LE-Curve is used to renovate the color cast of the learned images. To realize the superb detail of the repaired images, which is superior to the reference images, we develop a pixel fusion subnetwork (PF-SubNet) that adopts a pixel attention mechanism (PAM) to eliminate noise from the underwater image. The PAM adapts weight allocation to different levels of a feature map, which leads to an enhancement in the visibility of severely degraded areas. The experimental results show that the proposed LEPF-Net outperforms most of the existing underwater image enhancement methods. Furthermore, among the five classic no-reference image quality assessment (NRIQA) indicators, the enhanced images obtained by LEPF-Net are of higher quality than the ground truths from the UIEB dataset.