Due to the wavelength-dependent light attenuation, refraction and scattering, underwater images usually suffer from color distortion and blurred details. However, due to the limited number of paired underwater images with undistorted images as reference, training deep enhancement models for diverse degradation types is quite difficult. To boost the performance of data-driven approaches, it is essential to establish more effective learning mechanisms that mine richer supervised information from limited training sample resources. In this paper, we propose a novel underwater image enhancement network, called SGUIE-Net, in which we introduce semantic information as high-level guidance across different images that share common semantic regions. Accordingly, we propose semantic region-wise enhancement module to perceive the degradation of different semantic regions from multiple scales and feed it back to the global attention features extracted from its original scale. This strategy helps to achieve robust and visually pleasant enhancements to different semantic objects, which should thanks to the guidance of semantic information for differentiated enhancement. More importantly, for those degradation types that are not common in the training sample distribution, the guidance connects them with the already well-learned types according to their semantic relevance. Extensive experiments on the publicly available datasets and our proposed dataset demonstrated the impressive performance of SGUIE-Net. The code and proposed dataset are available at: https://trentqq.github.io/SGUIE-Net.html
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…
In this paper, we present a conditional generative adversarial network-based model for real-time underwater image enhancement. To supervise the adversarial training, we formulate an objective function that evaluates the perceptual image quality based on its global content, color, local texture, and style information. We also present EUVP, a large-scale dataset of a paired and unpaired collection of underwater images (of `poor' and `good' quality) that are captured using seven different cameras over various visibility conditions during oceanic explorations and human-robot collaborative experime…
PIGUIQA: A Physical Imaging Guided Perceptual Framework for Underwater Image Quality Assessment
Chongqing Research Institute of Harbin Institute of Technology（哈尔滨工业大学重庆研究所）；Harbin Institute of Technology（哈尔滨工业大学）；Faculty of Computing（计算机学院）；School of Computer Science（计算机科学学院）；University of Macau（澳门大学）；Institute for Infocomm Research（信息与通信研究所以）
CARNet: Collaborative Adversarial Resilience for Robust Underwater Image Enhancement and Perception
School of Software Technology, Dalian University of Technology（大连理工大学软件学院）；College of Information Science and Technology, Dalian Maritime University（大连海事大学信息科学技术学院）；School of Mechanical Engineering, Dalian University of Technology（大连理工大学机械工程学院）
Underwater Variable Zoom: Depth-Guided Perception Network for Underwater Image Enhancement
School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；University of New South Wales（新南威尔士大学）；School of Computer Science and Technology, Shandong Technology and Business University（山东工商学院计算机科学与技术学院）；School of Information and Communication Engineering, Dalian Minzu University（大连民族大学信息与通信工程学院）；School of Innovation Experiment, Dalian University of Technology（大连理工大学创新实验学院）
In this paper, we introduce and tackle the simultaneous enhancement and super-resolution (SESR) problem for underwater robot vision and provide an efficient solution for near real-time applications. We present Deep SESR, a residual-in-residual network-based generative model that can learn to restore perceptual image qualities at 2x, 3x, or 4x higher spatial resolution. We supervise its training by formulating a multi-modal objective function that addresses the chrominance-specific underwater color degradation, lack of image sharpness, and loss in high-level feature representation. It is also s…
Qingdao University（青岛大学）；Ocean University of China（中国海洋大学）
Achieving subjective and objective quality assessment of underwater images is of high significance in underwater visual perception and image/video processing. However, the development of underwater image quality assessment (UIQA) is limited for the lack of comprehensive human subjective user study with publicly available dataset and reliable objective UIQA metric. To address this issue, we establish a large-scale underwater image dataset, dubbed UID2021, for evaluating no-reference UIQA metrics. The constructed dataset contains 60 multiply degraded underwater images collected from various sour…
Fuzhou University（福州大学）；Peng Cheng Laboratory（鹏城实验室）；Beijing University of Technology（北京工业大学）；Beijing Artificial Intelligence Institute（北京人工智能研究院）；Université de Nantes（南特大学）；Laboratoire des Sciences du Numérique de Nantes（南特数字科学实验室）
The widespread image applications have greatly promoted the vision-based tasks, in which the Image Quality Assessment (IQA) technique has become an increasingly significant issue. For user enjoyment in multimedia systems, the IQA exploits image fidelity and aesthetics to characterize user experience; while for other tasks such as popular object recognition, there exists a low correlation between utilities and perceptions. In such cases, the fidelity-based and aesthetics-based IQA methods cannot be directly applied. To address this issue, this paper proposes a utility-oriented IQA in object rec…
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））
HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative Learning
School of Software Engineering, Dalian University of Technology（大连理工大学软件学院）；College of Information Science and Technology, Dalian Maritime University（大连海事大学信息科学技术学院）；Pazhou Laboratory (Huangpu)（琶洲实验室（黄埔））
Two-Stage Teacher-Student Reliable Prior Learning for Robust Underwater Image Enhancement
College of Future Information Technology, Fudan University（复旦大学未来信息技术学院）；Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））；Faculty of Robot Science and Engineering, Northeastern University（东北大学机器人科学与工程学院）；School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University（西北工业大学人工智能与光学电子学院（iOPEN））
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）
Downstream Task Inspired Underwater Image Enhancement: A Perception-Aware Study from Dataset Construction to Network Design
State Key Laboratory of Physical Oceanography, Ocean University of China（中国海洋大学物理海洋国家重点实验室）；Department of Electrical and Computer Engineering, Mississippi State University（密苏里州立大学电子与计算机工程系）
Style-Decoupled Adaptive Routing Network for Underwater Image Enhancement
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（测绘遥感信息工程国家重点实验室，武汉大学）；Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University（航空工程系，香港理工大学）
Underwater robots play an important role in oceanic geological exploration, resource exploitation, ecological research, and other fields. However, the visual perception of underwater robots is affected by various environmental factors. The main challenge now is that images captured by underwater robots are color-distorted. The hue of underwater images tends to be close to green and blue. In addition, the contrast is low and the details are fuzzy. In this paper, a new underwater image enhancement algorithm based on deep learning and image formation model is proposed. Experimental results show t…
Fraunhofer Institute for Computer Graphics Research (IGD)（弗劳恩霍夫计算机图形研究所）；University of Rostock（罗斯托克大学）
Visual inspection of underwater structures by vehicles, e.g. remotely operated vehicles (ROVs), plays an important role in scientific, military, and commercial sectors. However, the automatic extraction of information using software tools is hindered by the characteristics of water which degrade the quality of captured videos. As a contribution for restoring the color of underwater images, Underwater Denoising Autoencoder (UDAE) model is developed using a denoising autoencoder with U-Net architecture. The proposed network takes into consideration the accuracy and the computation cost to enable…
In this paper, we present a fast monocular depth estimation method for enabling 3D perception capabilities of low-cost underwater robots. We formulate a novel end-to-end deep visual learning pipeline named UDepth, which incorporates domain knowledge of image formation characteristics of natural underwater scenes. First, we adapt a new input space from raw RGB image space by exploiting underwater light attenuation prior, and then devise a least-squared formulation for coarse pixel-wise depth prediction. Subsequently, we extend this into a domain projection loss that guides the end-to-end learni…
We present an image dehazing algorithm with high quality, wide application, and no data training or prior needed. We analyze the defects of the original dehazing model, and propose a new and reliable dehazing reconstruction and dehazing model based on the combination of optical scattering model and computer graphics lighting rendering model. Based on the new haze model and the images obtained by the cameras, we can reconstruct the three-dimensional space, accurately calculate the objects and haze in the space, and use the transparency relationship of haze to perform accurate haze removal. To o…
