Underwater image enhancement has attracted much attention due to the rise of marine resource development in recent years. Benefit from the powerful representation capabilities of Convolution Neural Networks(CNNs), multiple underwater image enhancement algorithms based on CNNs have been proposed in the last few years. However, almost all of these algorithms employ RGB color space setting, which is insensitive to image properties such as luminance and saturation. To address this problem, we proposed Underwater Image Enhancement Convolution Neural Network using 2 Color Space (UICE^2-Net) that efficiently and effectively integrate both RGB Color Space and HSV Color Space in one single CNN. To our best knowledge, this method is the first to use HSV color space for underwater image enhancement based on deep learning. UIEC^2-Net is an end-to-end trainable network, consisting of three blocks as follow: a RGB pixel-level block implements fundamental operations such as denoising and removing color cast, a HSV global-adjust block for globally adjusting underwater image luminance, color and saturation by adopting a novel neural curve layer, and an attention map block for combining the advantages of RGB and HSV block output images by distributing weight to each pixel. Experimental results on synthetic and real-world underwater images show the good performance of our proposed method in both subjective comparisons and objective metrics. The code are available at https://github.com/BIGWangYuDong/UWEnhancement.
A Physical Model-Guided Framework for Underwater Image Enhancement and Depth Estimation
Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
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））
ASUS Intelligent Cloud Services, Asustek Computer Inc.（华硕电脑股份有限公司智能云服务）；National Taiwan University（国立台湾大学）
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 f…
City University of Hong Kong（香港城市大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Tianjin University（天津大学）；Beijing Jiaotong University（北京交通大学）
Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world images. It is thus unclear how these algorithms would perform on images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater image enhancement using large…
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）
On-board classification of underwater images using hybrid classical-quantum CNN based method
1 Department of Physics, Mahindra University, Hyderabad, 500043, Telangana, India；2 Department of Computer Science, Mahindra University, Hyderabad, 500043, Telangana, India；3 Department of Mechanical \& Aerospace Engineering, Mahindra University, Hyderabad, 500043, Telangana, India
Northeastern University（东北大学）；Warner Music Group（华纳音乐集团）
For aquaculture resource evaluation and ecological environment monitoring, automatic detection and identification of marine organisms is critical. However, due to the low quality of underwater images and the characteristics of underwater biological, a lack of abundant features may impede traditional hand-designed feature extraction approaches or CNN-based object detection algorithms, particularly in complex underwater environment. Therefore, the goal of this paper is to perform object detection in the underwater environment. This paper proposed a novel method for capturing feature information,…
Over the past few decades, underwater image enhancement has attracted increasing amount of research effort due to its significance in underwater robotics and ocean engineering. Research has evolved from implementing physics-based solutions to using very deep CNNs and GANs. However, these state-of-art algorithms are computationally expensive and memory intensive. This hinders their deployment on portable devices for underwater exploration tasks. These models are trained on either synthetic or limited real world datasets making them less practical in real-world scenarios. In this paper we propos…
Centre Universitaire Condorcet（孔多塞大学中心）；University of Burgundy（勃艮第大学）；University of Girona（赫罗纳大学）；Heriot-Watt University（赫瑞瓦特大学）
Autonomous repair of deep-sea coral reefs is a recent proposed idea to support the oceans ecosystem in which is vital for commercial fishing, tourism and other species. This idea can be operated through using many small autonomous underwater vehicles (AUVs) and swarm intelligence techniques to locate and replace chunks of coral which have been broken off, thus enabling re-growth and maintaining the habitat. The aim of this project is developing machine vision algorithms to enable an underwater robot to locate a coral reef and a chunk of coral on the seabed and prompt the robot to pick it up. A…
Complicated underwater environments bring new challenges to object detection, such as unbalanced light conditions, low contrast, occlusion, and mimicry of aquatic organisms. Under these circumstances, the objects captured by the underwater camera will become vague, and the generic detectors often fail on these vague objects. This work aims to solve the problem from two perspectives: uncertainty modeling and hard example mining. We propose a two-stage underwater detector named boosting R-CNN, which comprises three key components. First, a new region proposal network named RetinaRPN is proposed,…
Kyushu University（九州大学）；Hiroshima City University（广岛市立大学）
Demands on capturing dynamic scenes of underwater environments are rapidly growing. Passive stereo is applicable to capture dynamic scenes, however the shape with textureless surfaces or irregular reflections cannot be recovered by the technique. In our system, we add a pattern projector to the stereo camera pair so that artificial textures are augmented on the objects. To use the system at underwater environments, several problems should be compensated, i.e., refraction, disturbance by fluctuation and bubbles. Further, since surface of the objects are interfered by the bubbles, projected patt…
Kyushu University（九州大学）；Hiroshima City University（广岛市立大学）
Dense 3D shape acquisition of swimming human or live fish is an important research topic for sports, biological science and so on. For this purpose, active stereo sensor is usually used in the air, however it cannot be applied to the underwater environment because of refraction, strong light attenuation and severe interference of bubbles. Passive stereo is a simple solution for capturing dynamic scenes at underwater environment, however the shape with textureless surfaces or irregular reflections cannot be recovered. Recently, the stereo camera pair with a pattern projector for adding artifici…
In this paper, we apply the scattering transform (ST), a nonlinear map based off of a convolutional neural network (CNN), to classification of underwater objects using sonar signals. The ST formalizes the observation that the filters learned by a CNN have wavelet like structure. We achieve effective binary classification both on a real dataset of Unexploded Ordinance (UXOs), as well as synthetically generated examples. We also explore the effects on the waveforms with respect to changes in the object domain (e.g., translation, rotation, and acoustic impedance, etc.), and examine the consequenc…
Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement
College of Computer Science & Technology, Qingdao University（青岛大学计算机科学与技术学院）；College of Computer and Data Science, Fuzhou University（福州大学计算机与数据科学学院）；School of Computing and Mathematical Sciences, University of Leicester（莱斯特大学计算与数学科学学院）；School of Computer Science, University of Birmingham（伯明翰大学计算机科学学院）；Department of Computational Engineering, Lappeenranta-Lahti University of Technology (LUT), and Atmospheric Modelling Centre-Lahti（拉佩兰塔-拉赫蒂技术大学（LUT）计算工程系及拉赫蒂大气建模中心）；Computer Science Department, University of Exeter（埃克塞特大学计算机科学系）；Computer Science and Technology at Shandong Technology and Business University（山东科技职业大学计算机科学与技术）
Learning Heavily-Degraded Prior for Underwater Object Detection
DUT-RU International School of Information Science, Dalian University of Technology（大连理工大学-俄罗斯国立高等经济大学信息科学国际学院）；Dalian University of Technology（大连理工大学）
James Cook University（詹姆斯·库克大学）；National Research Council of Italy（意大利国家研究委员会）
Given a sufficiently large training dataset, it is relatively easy to train a modern convolution neural network (CNN) as a required image classifier. However, for the task of fish classification and/or fish detection, if a CNN was trained to detect or classify particular fish species in particular background habitats, the same CNN exhibits much lower accuracy when applied to new/unseen fish species and/or fish habitats. Therefore, in practice, the CNN needs to be continuously fine-tuned to improve its classification accuracy to handle new project-specific fish species or habitats. In this work…
Universidade Federal de Minas Gerais (UFMG)（米纳斯吉拉斯联邦大学）
The image processing community has witnessed remarkable advances in enhancing and restoring images. Nevertheless, restoring the visual quality of underwater images remains a great challenge. End-to-end frameworks might fail to enhance the visual quality of underwater images since in several scenarios it is not feasible to provide the ground truth of the scene radiance. In this work, we propose a CNN-based approach that does not require ground truth data since it uses a set of image quality metrics to guide the restoration learning process. The experiments showed that our method improved the vi…
Recovery of true color from underwater images is an ill-posed problem. This is because the wide-band attenuation coefficients for the RGB color channels depend on object range, reflectance, etc. which are difficult to model. Also, there is backscattering due to suspended particles in water. Thus, most existing deep-learning based color restoration methods, which are trained on synthetic underwater datasets, do not perform well on real underwater data. This can be attributed to the fact that synthetic data cannot accurately represent real conditions. To address this issue, we use an image to im…
Rutgers University（罗格斯大学）；Cleveland State University（克利夫兰州立大学）
New efficient source feature compression solutions are proposed based on a two-stage Walsh-Hadamard Transform (WHT) for Convolutional Neural Network (CNN)-based object classification in underwater robotics. The object images are firstly transformed by WHT following a two-stage process. The transform-domain tensors have large values concentrated in the upper left corner of the matrices in the RGB channels. By observing this property, the transform-domain matrix is partitioned into inner and outer regions. Consequently, two novel partitioning methods are proposed in this work: (i) fixing the siz…
Beijing Jiaotong University（北京交通大学）；University of South Carolina（南卡罗来纳大学）
Just like many other topics in computer vision, image classification has achieved significant progress recently by using deep-learning neural networks, especially the Convolutional Neural Networks (CNN). Most of the existing works are focused on classifying very clear natural images, evidenced by the widely used image databases such as Caltech-256, PASCAL VOCs and ImageNet. However, in many real applications, the acquired images may contain certain degradations that lead to various kinds of blurring, noise, and distortions. One important and interesting problem is the effect of such degradatio…
School of Ocean Engineering, Harbin Institute of Technology（哈尔滨工业大学（威海）海洋工程学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学（威海）计算机科学与技术学院）
With the continuous development of underwater vision technology, more and more remote sensing images could be obtained. In the underwater scene, sonar sensors are currently the most effective remote perception devices, and the sonar images captured by them could provide rich environment information. In order to analyze a certain scene, we often need to merge the sonar images from different periods, various sonar frequencies and distinctive viewpoints. However, the above scenes will bring nonlinear intensity differences to the sonar images, which will make traditional matching methods almost in…
Hochschule Bonn-Rhein-Sieg（波恩-莱茵-锡格应用技术大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）
Application of underwater robots are on the rise, most of them are dependent on sonar for underwater vision, but the lack of strong perception capabilities limits them in this task. An important issue in sonar perception is matching image patches, which can enable other techniques like localization, change detection, and mapping. There is a rich literature for this problem in color images, but for acoustic images, it is lacking, due to the physics that produce these images. In this paper we improve on our previous results for this problem (Valdenegro-Toro et al, 2017), instead of modeling feat…
Matching sonar images with high accuracy has been a problem for a long time, as sonar images are inherently hard to model due to reflections, noise and viewpoint dependence. Autonomous Underwater Vehicles require good sonar image matching capabilities for tasks such as tracking, simultaneous localization and mapping (SLAM) and some cases of object detection/recognition. We propose the use of Convolutional Neural Networks (CNN) to learn a matching function that can be trained from labeled sonar data, after pre-processing to generate matching and non-matching pairs. In a dataset of 39K training …
