IEEE Journal of Oceanic Engineering最新文献

{"title":"Underwater Image Enhancement Using Illuminant Intensity Compensation With Foreground Edge Map Rectification","authors":"Herng-Hua Chang;Pin-Yi Kuan","doi":"10.1109/JOE.2024.3523372","DOIUrl":"https://doi.org/10.1109/JOE.2024.3523372","url":null,"abstract":"Underwater image enhancement has been paid more attention in recent years as it is a fundamental task in many relevant image processing applications. This article investigates a new underwater image enhancement algorithm based on a simplified image formation model established by the integration of the Jaffe–McGlamery and Lambertian systems. The retinex theory is introduced into the prototype to explicitly disclose the illuminant intensity, which is computed using an efficient gray index scheme for light source attenuation compensation. Subsequently, an improved scene depth estimation method is exploited to separate the foreground from the background, upon which a foreground edge map is computed for better background light determination. Finally, an ensemble color gain is appraised to correct the color deviation. A wide variety of underwater images with various scenarios in six different data sets were employed to evaluate the proposed image enhancement system. Experimental results demonstrated the advantages of our underwater image enhancement algorithm over many state-of-the-art methods both qualitatively and quantitatively. It is believed that the developed image enhancement framework has potential in many underwater image processing applications.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"835-850"},"PeriodicalIF":3.8,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DenseNet-Based Robust Channel Estimation in OFDM for Improving Underwater Acoustic Communication","authors":"Songzuo Liu;Muhamamd Adil;Lu Ma;Suleman Mazhar;Gang Qiao","doi":"10.1109/JOE.2024.3510929","DOIUrl":"https://doi.org/10.1109/JOE.2024.3510929","url":null,"abstract":"Underwater acoustic (UWA) communication presents unique challenges due to the unpredictable and dynamic nature of acoustic channels, influenced by Doppler spread, low signal-to-noise ratios (SNRs), and the general need for complex channel characteristics, coupled with a scarcity of real-world data. Accurate orthogonal frequency division multiplexing (OFDM) channel estimation is pivotal for ensuring reliable data transmission in such challenging environments. In this study, we introduce the DenseNet estimator, which is specifically used for OFDM channel estimation in UWA communication. The use of dense connectivity within the DenseNet structure proves to be advantageous in capturing the intricacies of the complex and dynamic UWA channels. This architecture, showcasing robustness even when there's a limited number of pilots, sets it apart from conventional methods. The DenseNet estimator is trained on the WATERMARK data set, leveraging the richness of real-time varying channel impulse responses to provide the necessary diversity for accurate channel estimation. Uniquely, once trained, our DenseNet estimator operates without necessitating additional channel statistics like SNR, relying solely on the received signal as its primary input. This approach offers a simplified and more direct application in real-world scenarios. Our numerical results underscore the DenseNet estimator's efficacy: It consistently outperforms traditional methods such as least square, minimum mean square error, and fully connected neural network, recording improvements of up to 96.3%, 94.2%, and 40.0% in bit error rate. Performance assessments across various watermark underwater channels demonstrate the DenseNet estimator's adaptability and robustness in both stable and challenging environments.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1518-1537"},"PeriodicalIF":3.8,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Transmission Loss Predictions in Acoustic Field Experiments","authors":"Ryan A. McCarthy;Sophia T. Merrifield;Jit Sarkar;Ryan Bednar;Andy Nager;Charles Brooks;Derek Ung;Jack Donohoe;Eric J. Terrill","doi":"10.1109/JOE.2024.3498007","DOIUrl":"https://doi.org/10.1109/JOE.2024.3498007","url":null,"abstract":"This study evaluates the capabilities of three distinct transmission loss (TL) models to use as an on-board decision aid to identify locations in depth and range for an autonomous underwater vehicle (AUV) and an uncrewed surface vehicle (USV) to improve acoustic communications. AUVs and USVs are computationally and resource limited vehicles that require fast in-situ decisions to reposition. This work utilizes an experiment in August 2023 off the coast of Southern California of an SV3 Wave Glider communicating with a REMUS 100 vehicle equipped with a 25 kHz acoustic modem. Observations of TL across a range dependent bathymetry are used to evaluate various decision aid models that can provide acoustical awareness for the vehicles. Models are based on: first, simple approximations of sound intensity that decreases at range; second, a ray-based physics model Bellhop; and third, a machine learning (ML) decision tree model trained off-board the vehicle using Bellhop simulations. Predictions from the ML model show good agreement with the observed field data with an overall accuracy of 85.42%. Although there are discrepancies between locations of acceptable TL and those observed by the vehicles, the ML model is able to perform as well as the Bellhop model while improving the prediction speed of TL at an individual range and depth from 764.23 to 0.57 ms.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 3","pages":"1668-1675"},"PeriodicalIF":3.8,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utility-Centered Underwater Image Quality Evaluation","authors":"Weiling Chen;Honggang Liao;Rongfu Lin;Tiesong Zhao;Ke Gu;Patrick Le Callet","doi":"10.1109/JOE.2024.3498273","DOIUrl":"https://doi.org/10.1109/JOE.2024.3498273","url":null,"abstract":"In recent decades, the emergence of image applications has greatly facilitated the development of vision-based tasks. As a result, image quality assessment (IQA) has become increasingly significant for monitoring, controlling, and improving visual signal quality. While existing IQA methods focus on image fidelity and aesthetics to characterize perceived quality, it is important to evaluate the utility-centered quality of an image for popular tasks, such as object detection. However, research shows that there is a low correlation between utilities and perceptions. To address this issue, this article proposes a utility-centered IQA approach. Specifically, our research focuses on underwater fish detection as a challenging task in an underwater environment. Based on this task, we have developed a utility-centered underwater image quality database (UIQD) and a transfer learning-based advanced underwater quality by utility assessment (AQUA). Inspired by the top–down design approach used in fidelity-oriented IQA methods, we utilize deep models of object detection and transfer their features to the mission of utility-centered quality evaluation. Experimental results validate that the proposed AQUA achieves promising performance not only in fish detection but also in other tasks such as face recognition. We believe that our research provides valuable insights to bridge the gap between IQA research and visual tasks.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"743-757"},"PeriodicalIF":3.8,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility Study on the Elimination of Force-Position Coupling in Steering Systems With a Novel Compound Rotary Vane Steering Gear","authors":"Chang Yuan;Jianxing Zhang;Baoren Li;Yuxuan Peng;Zhaozhuo Wang","doi":"10.1109/JOE.2024.3516094","DOIUrl":"https://doi.org/10.1109/JOE.2024.3516094","url":null,"abstract":"In this article, a novel compound rotary vane steering gear actuator was designed to solve the problem of strong force-position coupling between the rudder blade and hydrodynamic force during the steering process. The actuator applies active torque to the rudder drive cylinder through a torque decoupling cylinder, so as to eliminate the load torque generated by the hydraulic force on the rudder drive cylinder. The simulation and experiment results show that compared with a single-layer rotary vane steering gear, the compound rotary vane steering gear has faster steering speed, higher position accuracy, and stronger disturbance rejection capability under the influence of hydrodynamic loads. Under disturbances of hydrodynamic load, the average time for the compound rotary vane steering gear to reach steady state is reduced by 37.45%, and the steady-state error is less than 0.1°. When the impact load is encountered, the average stability time is reduced by 41.45%, thus verifying the principle of eliminating load by structure. The compound rotary vane steering gear demonstrated excellent maneuvering performance when applied to steering systems with large inertia and strong nonlinearity.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1196-1209"},"PeriodicalIF":3.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Ocean Gradient Tracking Using an Autonomous Underwater Vehicle With a Boundless Model","authors":"Tore Mo-Bjørkelund;Renato Mendes;Francisco López-Castejón;Martin Ludvigsen","doi":"10.1109/JOE.2024.3484577","DOIUrl":"https://doi.org/10.1109/JOE.2024.3484577","url":null,"abstract":"This work presents a method for exploring a dynamic river plume boundary using an autonomous underwater vehicle with an on-board lightweight <italic>boundless</i> model. The <italic>boundless</i> approach is achieved by not constraining the path evaluations or the Gaussian random field to a predefined geographical area. In-situ decision-making enables targeted sampling of the ocean–river plume interaction. The data-driven and adaptive approaches provide the capability and opportunity to fully utilize the operational window for the vehicle. The method was developed using a simulated plume and vehicle, and results from simulation studies and successful field trials from the Douro River plume outside Porto, Portugal, are presented. The vehicle adapts its path based on the underway real-time assimilation of measurements, seeking to gain new information while not straying away from the front. Owing to the unpredictable shape and size of the river front, a model-based <italic>boundless</i> method for adaptive sampling was constructed, generating potential waypoints as a function of the vehicle's position and the accumulated knowledge of the plume. Not bounding the spatial or geographical extent of the method allows for greater variation in plume shape and size. The river plume's extent is defined here as the area within the sharpest spatial salinity gradient, containing less saline water than the surrounding ocean. In the method, the depth of the sharpest vertical salinity gradient, or plume depth, is estimated using a 2-D <italic>Gaussian Process</i>, where the plume depth is estimated from a dive and ascent envelope of the robot traversing the ocean in an undulating fashion. Computational efficiency is gained from the resulting low number of inputs to the Gaussian process, compared to the number of salinity measurements, ensuring rapid on-board adaption. The next waypoint is chosen as the first waypoint in a path that maximizes the weighted sum of uncertainty, estimated plume depth, and the absolute value of the difference between the current plume depth and the estimated river plume depth along the path. This encourages traversal of the plume in a fashion that enables the extent of the plume to be resolved in high detail. The data-driven method was field verified in the Douro River, proving the ability to track the river plume to balance exploration and exploitation behavior to maximize the information value of the mission in real time onboard the vehicle.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"955-967"},"PeriodicalIF":3.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10879136","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semi-Supervised Underwater Image Enhancement Network Boosted by Depth Map Consistency","authors":"Fengqi Xiao;Jiahui Liu;Yifan Huang;En Cheng;Fei Yuan","doi":"10.1109/JOE.2024.3487350","DOIUrl":"https://doi.org/10.1109/JOE.2024.3487350","url":null,"abstract":"Underwater optical images are a crucial information source for autonomous underwater vehicles during underwater development and exploration. When these vehicles are in operation, they need to capture high-quality images for information extraction and analysis and require depth map information from the underwater scene to maintain the vehicle's posture balance, obstacle avoidance, and navigation. However, the absorption and scattering of light in water result in low-quality underwater images, significantly affecting the execution of these tasks. In response to these challenges, this article proposes a physically guided, semi-supervised dual-loop network for underwater image enhancement. This network is designed to accomplish high-quality underwater image enhancement and depth map estimation simultaneously. First, the revised underwater image formation model is employed to guide a two-stage network in decomposing and reconstructing underwater images. The depth map consistency of the scene and piecewise cycle consistency loss are utilized to ensure the reliability of the image transformation process. In another loop, a self-augmentation module based on inherent optical properties is introduced to enhance the robustness of the decomposition network. A multimodal discriminator is incorporated to form piecewise adversarial loss to improve the visual quality of the images. Through extensive experimental evaluation and analysis, the proposed method not only demonstrates outstanding performance in underwater image enhancement and depth map estimation but also reveals the relationships between various physical quantities during the degradation process of underwater images, enhancing the physical interpretability of the neural network.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"795-816"},"PeriodicalIF":3.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical Wavelet Decomposition Network for Water-Related Optical Image Enhancement","authors":"Jingchun Zhou;Rui Zhou;Zongxin He;Cong Zhang;Qiuping Jiang;Weishi Zhang;Ferdous Sohel","doi":"10.1109/JOE.2024.3458349","DOIUrl":"https://doi.org/10.1109/JOE.2024.3458349","url":null,"abstract":"Enhancing water-related optical images poses a significant challenge due to the complex interplay of direct attenuation and backscattering. Current methods primarily focus on modifying the spatial domain and pay less attention to the heterogeneity of the frequency domain degradation distributions, which limits their effectiveness in solving multiple types of degradation problems simultaneously. To overcome these limitations, we propose a hierarchical wavelet decomposition network (HWD-Net). HWD-Net leverages wavelet transforms to create a compact feature space, enabling the distinct restoration of low and high-frequency degradations through a strategic divide-and-conquer approach, which prevents the interaction of high- and low-frequency information and avoids the generation of incorrect textures. Furthermore, HWD-Net employs a hierarchical decomposition paradigm to progressively extract richer high-frequency information, achieving superior enhancements in a coarse-to-fine manner. Comprehensive evaluations on multiple underwater data sets demonstrate the superiority of HWD-Net over state-of-the-art methods in terms of image quality and inference time.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"776-794"},"PeriodicalIF":3.8,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inversion of Time-Evolving Sound Speed Profiles by DREAM-zs With QPSO Proposal Distribution","authors":"Ye Wang;Jianlong Li;Jing Chen;Yida Xu;Lingzao Zeng","doi":"10.1109/JOE.2024.3507825","DOIUrl":"https://doi.org/10.1109/JOE.2024.3507825","url":null,"abstract":"Sound speed profile (SSP) inversion using acoustic data is an efficient approach for estimating SSPs, though it presents a nonlinear and non-Gaussian problem. To improve the spatial resolution of SSP inversion, a range-dependent environment is considered, which increases the number of parameters to be estimated. These challenges limit the performance of commonly used methods, such as the ensemble Kalman filter (EnKF) and particle filter (PF). While EnKFs can handle high-dimensional problems, they assume Gaussian probability distributions. PFs are better suited for highly nonlinear and non-Gaussian systems but are generally more computationally intensive. DiffeRential evolution adaptive Metropolis (DREAM)-zs, a Markov chain Monte Carlo method, is effective for approximating high-dimensional probability distributions, although its convergence speed decreases rapidly as the dimensionality of the parameter space increases. Quantum-behaved particle swarm optimization (QPSO), which combines principles from quantum mechanics and PSO, is a probabilistic optimization algorithm that has been proven successful in various optimization problems. To enhance the efficiency of DREAM-zs, the QPSO proposal distribution is embedded during the burn-in period, forming the DREAM-zqs method. The proposed method is used to track time-evolving SSPs in a range-dependent environment. The inversion problem is formulated in a state-space form, integrating data from the regional ocean modeling system to improve efficiency by shifting the state vector. Simulations and experimental results demonstrate that both DREAM-zs and DREAM-zqs outperform EnKF and PF, with DREAM-zqs achieving faster convergence than DREAM-zs while maintaining inversion accuracy.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1403-1418"},"PeriodicalIF":3.8,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UVTD: A Large-Scale Multilabel Data Set for Underwater Vision Tasks","authors":"Zhengyong Wang;Meng Yu;Lei Cao;Pengyu Liu;Linfeng Wang;Xiang Li;Yixiao Hong;Chang He;Liquan Shen","doi":"10.1109/JOE.2024.3503664","DOIUrl":"https://doi.org/10.1109/JOE.2024.3503664","url":null,"abstract":"With the exploration of ocean resources, underwater vision tasks (UVTs) have attracted increasing interest in recent years. However, the advancement of UVTs is hampered by several challenges, prominently the difficulty in acquiring large-scale data sets with accurate annotations, and the absence of a unified multi-label data set for underwater multitask learning (UMTL). Motivated by the critical need for a large-scale, multilabel data set tailored for UVTs, we present a large-scale multilabel data set for underwater vision tasks (UVTD), which offers solutions to several bottlenecks in UVT research: first, supporting diverse applications. With annotations for two underwater low-level vision tasks (i.e., underwater image enhancement and underwater image quality assessment) and three high-level vision tasks (i.e., underwater semantic segmentation, underwater object detection, and underwater salient object detection), UVTD supports a wide range of underwater applications. Second, enhancing model generalization. UVTD comprises 5380 real underwater images, covering diverse scenes and varied degradation characteristics, improving the robustness of vision algorithms. Finally, facilitating multitask learning. Our multilabel data set enables researchers to explore the correlations between tasks and develop robust UMTL algorithms. Based on UVTD, we propose two UMTL networks tailored to the low-level and high-level tasks separately, serving as benchmarks for future research in the UMTL field. Extensive experiments demonstrate UVTD's superiority across multiple UVTs, and the proposed UMTL networks exhibit competitive performance on these tasks, implying the significant implications of UVTD for future research.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"898-918"},"PeriodicalIF":3.8,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}