Underwater image enhancement is an important low-level computer vision task for autonomous underwater vehicles and remotely operated vehicles to explore and understand the underwater environments. Recently, deep convolutional neural networks (CNNs) have been successfully used in many computer vision problems, and so does underwater image enhancement. There are many deep-learning-based methods with impressive performance for underwater image enhancement, but their memory and model parameter costs are hindrances in practical application. To address this issue, we propose a lightweight adaptive feature fusion network (LAFFNet). The model is the encoder-decoder model with multiple adaptive feature fusion (AAF) modules. AAF subsumes multiple branches with different kernel sizes to generate multi-scale feature maps. Furthermore, channel attention is used to merge these feature maps adaptively. Our method reduces the number of parameters from 2.5M to 0.15M (around 94% reduction) but outperforms state-of-the-art algorithms by extensive experiments. Furthermore, we demonstrate our LAFFNet effectively improves high-level vision tasks like salience object detection and single image depth estimation.
Underwater images are inevitably affected by color distortion and reduced contrast. Traditional statistic-based methods such as white balance and histogram stretching attempted to adjust the imbalance of color channels and narrow distribution of intensities a priori thus with limited performance. Recently, deep-learning-based methods have achieved encouraging results. However, the involved complicate architecture and high computational costs may hinder their deployment in practical constrained platforms. Inspired by above works, we propose a statistically guided lightweight underwater image en…
Five A$^{+}$ Network: You Only Need 9K Parameters for Underwater Image Enhancement
National University of Singapore（新加坡国立大学）；ZJU-UIUC Institute, Zhejiang University（浙江大学ZJU-UIUC研究院）；Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing（福建省海洋信息感知与智能处理重点实验室）
Real-Time Underwater Image Enhancement via Frequency-Guided Dual-Path Attention
Glasgow College, UESTC, Chengdu, China（格拉斯哥学院，UESTC，成都，中国）；School of Information and Communication Engineering, UESTC, Chengdu, China（信息与通信工程学院，UESTC，成都，中国）；University of Glasgow, Glasgow, UK（格拉斯哥大学，格拉斯哥，英国）
Underwater visual perception is essentially important for underwater exploration, archeology, ecosystem and so on. The low illumination, light reflections, scattering, absorption and suspended particles inevitably lead to the critically degraded underwater image quality, which causes great challenges on recognizing the objects from the underwater images. The existing underwater enhancement methods that aim to promote the underwater visibility, heavily suffer from the poor image restoration performance and generalization ability. To reduce the difficulty of underwater image enhancement, we intr…
Institute of Artificial Intelligence (TeleAI), China Telecom（人工智能研究院（TeleAI），中国电信）；College of Computer and Control Engineering, Northeast Forestry University（东北林业大学计算机与控制工程学院）；College of Future Information Technology, Fudan University（复旦大学未来信息技术学院）；School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University（西北工业大学人工智能、光学与电子学院（iOPEN））；Fujian Ocean Innovation Center, Xiamen（福建海洋创新中心，厦门）
DACA-Net: A Degradation-Aware Conditional Diffusion Network for Underwater Image Enhancement
The Hong Kong University of Science and Technology (Guangzhou), Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)（香港科学与技术大学（广州）、广东海洋工程科学实验室（广州））；The Hong Kong University of Science（香港科学与技术大学）；Engineering Guangdong Laboratory (Guangzhou)（广东工程实验室（广州））
Underwater Image Restoration via Polymorphic Large Kernel CNNs
University of Macau（澳门大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen Polytechnic University（深圳职业技术大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
LUIEO: A Lightweight Model for Integrating Underwater Image Enhancement and Object Detection
School of Mathematics, North University of China（中北大学数学学院）；College of Computer and Information Technology, Shanxi University（山西大学计算机与信息技术学院）；School of Mathematics Sciences, Beijing Normal University（北京师范大学数学科学学院）
Adaptive Enhancement and Dual-Pooling Sequential Attention for Lightweight Underwater Object Detection with YOLOv10
1,2Department of Computer Science；Engineering, Dhaka University of Engineering \& Technology Gazipur, 1707, Bangladesh 3Department of Computer Science；Engineering, BRAC University Dhaka, Bangladesh
To assist underwater object detection for better performance, image enhancement technology is often used as a pre-processing step. However, most of the existing enhancement methods tend to pursue the visual quality of an image, instead of providing effective help for detection tasks. In fact, image enhancement algorithms should be optimized with the goal of utility improvement. In this paper, to adapt to the underwater detection tasks, we proposed a lightweight dynamic enhancement algorithm using a contribution dictionary to guide low-level corrections. Dynamic solutions are designed to captur…
Rethinking Conditional Generation for Underwater Salient Object Detection
School of Computer Science and Technology, Hainan University（海南大学计算机科学与技术学院）；Key Laboratory of Machine Intelligence and System Control, Ministry of Education, Shandong University（山东大学机器智能与系统控制教育部重点实验室）；School of Data Science, Lingnan University（岭南大学数据科学学院）
Dalian University of Technology（大连理工大学）；The Hong Kong Polytechnic University（香港理工大学）
Single image dehazing is an important low-level vision task with many applications. Early researches have investigated different kinds of visual priors to address this problem. However, they may fail when their assumptions are not valid on specific images. Recent deep networks also achieve relatively good performance in this task. But unfortunately, due to the disappreciation of rich physical rules in hazes, large amounts of data are required for their training. More importantly, they may still fail when there exist completely different haze distributions in testing images. By considering the …
GeoCueFormer: Geometry-Guided Wavelet Representation and Prediction-Cued Dual-Stage Decoder for Underwater Semantic Segmentation
Southwest University of Science and Technology（西南科技大学）；Columbia University（哥伦比亚大学）；Carnegie Mellon University（卡内基梅隆大学）；University of California, Berkeley（加州大学伯克利分校）
Learning Spatially Adaptive Structural Coordination for Underwater Salient Object Detection
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（电子与计算机工程系，香港科学与技术大学）；School of Robotics and Advanced Manufacture, Harbin Institute of Technology（机器人与先进制造学院，哈尔滨工业大学）；School of Computation, Information and Technology, Technical University of Munich（计算、信息与技术学院，慕尼黑技术大学）；College of Computer Science & Visual Computing and Intelligent Perception Lab, Nankai University（计算机科学与视觉计算学院及智能感知实验室，南开大学）；School of Cyber Science and Technology, Sun Yat-sen University（网络科学与技术学院，中山大学）；School of Automation, Southeast University（自动化学院，东南大学）
You Sense Only Once Beneath: Ultra-Light Real-Time Underwater Object Detection
School of Data Science and Engineering, and Xingzhi College, South China Normal University（数据科学与工程学院和行知学院，华南师范大学）；School of Physics, South China Normal University（物理学院，华南师范大学）；School of Electronics and Information Engineering, and Xingzhi College, South China Normal University（电子与信息工程学院和行知学院，华南师范大学）
EPANet: Efficient Path Aggregation Network for Underwater Fish Detection
Dalian Key Laboratory of Smart Fishery（大连智能渔业重点实验室）；College of Information Engineering, Dalian Ocean University（大连海洋大学信息工程学院）；Key Laboratory of Environment Controlled Aquaculture（环境可控水产养殖重点实验室）；Dalian Polytechnic University School of International Education（大连职业技术学院国际教育学院）
SU-YOLO: Spiking Neural Network for Efficient Underwater Object Detection
College of Computer and Information Technology, China Three Gorges University（三峡大学计算机与信息学院）；Wuhan University of Technology（武汉理工大学）；Peking University（北京大学）
FinSight-Net:A Physics-Aware Decoupled Network with Frequency-Domain Compensation for Underwater Fish Detection in Smart Aquaculture
Dalian Key Laboratory of Smart Fishery（大连智能渔业重点实验室）；College of Information Engineering, Dalian Ocean University（大连海洋大学信息工程学院）；Key Laboratory of Environment Controlled Aquaculture（环境可控养殖重点实验室）；Dalian Polytechnic University School of International Education（大连职业技术学院国际教育学院）
