IEEE Journal of Oceanic Engineering最新文献_第9页

Field Evaluation of a MEMS-Based Silicon Resonant Pressure Sensor for Seafloor Pressure Observation 用于海底压力观测的mems硅谐振压力传感器的现场评估

IF 5.6 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2026-07-01 Epub Date: 2026-06-05 DOI: 10.1109/JOE.2026.3674030

Masanao Shinohara;Takashi Kunugi;Shin Aoi;Shigeto Iwai;Takashi Yoshida;Toshihiko Oomachi;Ryuichiro Noda;Tetsuya Takeda;Masashi Mochizuki;Kenji Uehira;Tomoaki Yamada

{"title":"Field Evaluation of a MEMS-Based Silicon Resonant Pressure Sensor for Seafloor Pressure Observation","authors":"Masanao Shinohara;Takashi Kunugi;Shin Aoi;Shigeto Iwai;Takashi Yoshida;Toshihiko Oomachi;Ryuichiro Noda;Tetsuya Takeda;Masashi Mochizuki;Kenji Uehira;Tomoaki Yamada","doi":"10.1109/JOE.2026.3674030","DOIUrl":"https://doi.org/10.1109/JOE.2026.3674030","url":null,"abstract":"Seafloor pressure observations mainly use precise quartz resonant sensors at present due to their high sensitivity. Recently, a new silicon resonant sensor using microelectromechanical systems (MEMS) technology has emerged with low-power consumption, compact size, constant quality, and high sensitivity. High stability is expected due to the small size of a silicon resonant pressure sensor, and interunit variability among sensors is believed to be small because of the MEMS technology. The influence of the movement of the sensor itself is small due to the mechanism of pressure transmission. Nankai Trough seafloor observation network for earthquakes and tsunamis, named N-net, is equipped with the new sensors. Before routine observations using this new sensor, we evaluate the performance of the new silicon resonant sensor for seafloor pressure observation. An existing free-fall pop-up type ocean bottom pressure gauge has a quartz crystal resonant sensor. We replaced the sensor with the new silicon resonant pressure sensor and performed seafloor observations off the Boso peninsula near Tokyo. We obtained pressure data from two new sensors. Noise spectra of the new sensor were compared to those of the ordinary crystal sensor, which was installed at the same position. The new and ordinary gauges have identical noise levels at periods longer than about 50 s. Although the new silicon gauges seem to have slightly higher noise levels at periods between 50 and 10 s, the new pressure gauge can detect pressure changes larger than 1 hPa throughout the whole observation band. The new pressure gauge observed sea level fluctuation by volcanic eruption in Tonga and the identical waveforms were obtained from the new and ordinary pressure gauges.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"2137-2146"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11552632","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148451940","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}

引用次数: 0

Enhancing Path Following of Underactuated Water-Cleaning ASVs Using Adaptive MPC: Real-Time Parameter Estimation and Sensitivity Analysis in Dynamic Marine Environments 基于自适应MPC增强欠驱动清水asv路径跟踪：动态海洋环境下实时参数估计与灵敏度分析

IF 5.6 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2026-07-01 Epub Date: 2026-06-05 DOI: 10.1109/JOE.2026.3674014

Abdelhaleem Saad;Dong Trong Nguyen;Waseem Akram;Shubham Garg;Irfan Hussain

{"title":"Enhancing Path Following of Underactuated Water-Cleaning ASVs Using Adaptive MPC: Real-Time Parameter Estimation and Sensitivity Analysis in Dynamic Marine Environments","authors":"Abdelhaleem Saad;Dong Trong Nguyen;Waseem Akram;Shubham Garg;Irfan Hussain","doi":"10.1109/JOE.2026.3674014","DOIUrl":"https://doi.org/10.1109/JOE.2026.3674014","url":null,"abstract":"Autonomous surface vehicles (ASVs) offer a scalable solution for marine debris collection, but their dynamics change as trash accumulates. This work explores adaptive control for underactuated water-cleaning ASVs using model-predictive control (MPC) with real-time parameter estimation via moving horizon estimation (MHE). While MHE effectively tracked linear drag variations, it required sufficient excitation for added mass estimation and was computationally expensive. We also analyzed the sensitivity of explicit hydrodynamic estimation to measurement noise and control inputs, revealing potential inaccuracies. A thorough sensitivity study on nominal MPC under varying hydrodynamic conditions, based on the assumption of simulated trash accumulation effects on system dynamics, motivated the development of an adaptive MPC controller, which showed better performance in extreme scenarios (e.g., heavy trash accumulation and strong currents). Field experiments further indicated that wind-induced currents had a greater impact on MPC performance than trash accumulation. In addition, we developed a comprehensive Python simulation platform to incorporate real-time disturbances, highlighting MPC’s limitations when the model is inaccurate. These findings underscore the need for robust control strategies in dynamic marine environments, paving the way for improved autonomy in water-cleaning ASVs.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"1767-1786"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148451926","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}

引用次数: 0

Physics-Informed Ensemble Learning for Detection of Malfunctioning Ocean Bottom Pressure Gauges: A Case Study on the S-Net Network 基于物理信息的集成学习在海底压力表故障检测中的应用——以S-Net网络为例

IF 5.6 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2026-07-01 Epub Date: 2026-06-03 DOI: 10.1109/JOE.2026.3686725

Oleg V. Ponomarev;Nikita V. Belyakov;Sergey V. Kolesov;Andrei M. Volgarev

{"title":"Physics-Informed Ensemble Learning for Detection of Malfunctioning Ocean Bottom Pressure Gauges: A Case Study on the S-Net Network","authors":"Oleg V. Ponomarev;Nikita V. Belyakov;Sergey V. Kolesov;Andrei M. Volgarev","doi":"10.1109/JOE.2026.3686725","DOIUrl":"https://doi.org/10.1109/JOE.2026.3686725","url":null,"abstract":"In this study, we analyze bottom pressure time series from 139 operational stations of the S-net network to detect potential anomalies. As of 12 March 2025, 15 stations were identified as malfunctioning: S1N09, S1N13, S1N21, S2N06, S2N18, S2N20, S2N25, S3N02, S3N04, S3N22, S4N10, S4N24, S5N10, S5N12, and S5N16. Notably, the failure of station S2N25 was captured in real time during observations. The analysis revealed that increased depth is statistically associated with a higher risk of sensor malfunction (<inline-formula><tex-math>$p < 0.05$</tex-math></inline-formula>). To enable automatic identification of malfunctioning pressure gauges, we introduce an ensemble learning model that combines three complementary reconstruction techniques—Seasonal Auto Regressive Integrated Moving Average forecasting, physics-based tidal prediction using the TPXO model, and interstation similarity analysis—evaluated with two error metrics (mean squared error and DTW). Their outputs are integrated by a lightweight random-forest meta-classifier, yielding an interpretable and data-efficient hybrid model. The model aggregates prior knowledge of the physical characteristics of the signal with machine learning to classify stations as either operational or malfunctioning. The average MCC-score on the test set reached 0.90 with a standard deviation of 0.06. Because the proposed model is fully interpretable and computationally inexpensive, it is suitable for real-time deployment within existing tsunami warning infrastructures.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"2147-2158"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148451944","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}

引用次数: 0

Active Platform Stabilization of Floating Offshore Wind Turbines Under Harsh Marine Environments 恶劣海洋环境下浮式海上风力发电机主动平台稳定研究

IF 5.6 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2026-07-01 Epub Date: 2026-06-18 DOI: 10.1109/JOE.2026.3695322

Shuzhen Li;Keum-Shik Hong;Xian Li

{"title":"Active Platform Stabilization of Floating Offshore Wind Turbines Under Harsh Marine Environments","authors":"Shuzhen Li;Keum-Shik Hong;Xian Li","doi":"10.1109/JOE.2026.3695322","DOIUrl":"https://doi.org/10.1109/JOE.2026.3695322","url":null,"abstract":"This article investigates posture control strategies for floating offshore wind turbines operating in harsh marine environments. A novel thruster-assisted triangular platform is modeled using a four-degree-of-freedom representation that captures heave, roll, pitch, and tower deflection dynamics. To facilitate controller synthesis, a reduced three-degree-of-freedom model is derived and employed to design a sliding-mode controller that integrates equivalent control with a supertwisting algorithm. The proposed scheme enhances robustness and stability under significant wind and wave disturbances. Numerical simulations conducted under calm, moderate, and severe sea states demonstrate that the proposed sliding-mode controller substantially outperforms a conventional proportional–integral controller, yielding marked reductions in platform oscillations and improved transient behavior. The enhanced vibration attenuation and faster stabilization indicate that the proposed integrated approach can effectively improve energy capture efficiency and extend the operational lifespan of floating offshore wind turbines, supporting its suitability for deep-sea deployment.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"2077-2090"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148451952","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}

引用次数: 0

CCCSIN: Color Compensation and Cross-Scale Semantic Information-Guided Network for Underwater Image Enhancement 彩色补偿和跨尺度语义信息引导的水下图像增强网络

IF 5.6 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2026-07-01 Epub Date: 2026-06-25 DOI: 10.1109/JOE.2026.3695780

Ning Wang;Tingkai Chen;Fang Dong

{"title":"CCCSIN: Color Compensation and Cross-Scale Semantic Information-Guided Network for Underwater Image Enhancement","authors":"Ning Wang;Tingkai Chen;Fang Dong","doi":"10.1109/JOE.2026.3695780","DOIUrl":"https://doi.org/10.1109/JOE.2026.3695780","url":null,"abstract":"In this article, to simultaneously address color distortion and content reconstruction pertaining to underwater image enhancement, a color compensation and cross-scale semantic information-guided network (CCCSIN) is devised. The multidense connection residual block (MCRB) with spatial and pixel attentions in a cascading pattern is developed, such that color distortion of underwater image can be effectively alleviated. By virtue of global and local semantic information derived from the semantic segmentation model, the cross-scale semantic feature enhancement module (CSFEM) is created, such that detailed image content can be accurately reconstructed. Employing the supervision of absolute error and mean square error loss functions, the entire CCCSIN scheme is constructed by organically combining with MCRB and CSFEM. Experiments demonstrate the effectiveness and superiority of the proposed CCCSIN scheme, peak signal-to-noise ratios are 3.2981, 2.2953, 2.1711, 5.4679, 7.3372, 9.5632, 4.1839, and 2.4253 higher than those of contrast limited adaptive histogram equalization, integrated colour model (ICM), unsupervised correction method (UCM), dark channel prior (DCP), image blurring and light absorption, underwater DCP, underwater light attenuation prior, and fast underwater image enhancement with generative adversarial networks (FunIE-GAN) frameworks, respectively, demonstrating compound advantages in detail recovery, texture preservation, color correction, contrast improvement, and clarity enhancement.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"2465-2477"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148451983","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}

引用次数: 0

WADNet: Improving Direction-of-Arrival Estimation in Multipath Underwater Settings Using Wavelet Scattering Transform and Self-Attention Mechanism WADNet：利用小波散射变换和自注意机制改进水下多径环境下的到达方向估计

IF 5.6 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2026-07-01 Epub Date: 2026-03-06 DOI: 10.1109/JOE.2026.3674394

Murtiza Ali;Karan Nathwani

{"title":"WADNet: Improving Direction-of-Arrival Estimation in Multipath Underwater Settings Using Wavelet Scattering Transform and Self-Attention Mechanism","authors":"Murtiza Ali;Karan Nathwani","doi":"10.1109/JOE.2026.3674394","DOIUrl":"https://doi.org/10.1109/JOE.2026.3674394","url":null,"abstract":"Accurately determining the direction of arrival (DoA) for multiple underwater sources in noisy and multipath conditions is a major challenge in underwater acoustics. Conventional approaches often struggle with noise and multipath interference, leading to underdetermined scenarios and reduced accuracy. This study introduces the wavelet-attention DoA network (WADNet), a deep neural network that combines a multihead self-attention mechanism with a wavelet scattering transform to generate robust feature maps. The WADNet effectively captures global variations through self-attention and localized distortions via the 2-D wavelet transform, isolating multipath-induced artifacts that are often obscured in global covariance matrix analyses. The model is trained on a comprehensive simulated data set incorporating noise and varying multipath signals in a shallow isotropic channel, utilizing multipath-resistant covariance features to mitigate noise and multipath effects. WADNet’s performance is assessed using root-mean-square error and resolution probability (<inline-formula><tex-math>${P}_{text{rp}}$</tex-math></inline-formula>) across diverse ranges, signal-to-noise ratios, and multipath conditions. Furthermore, the WADNet exhibits strong generalization in real-time testing during the SWellEx-96 experiment, surpassing traditional methods in single-source (<inline-formula><tex-math>$S5$</tex-math></inline-formula>) and double-source (<inline-formula><tex-math>$S59$</tex-math></inline-formula>) scenarios. It delivers higher resolution and precise DoA estimates without needing domain adaptation or transfer learning, effectively bridging the gap between simulation and real-world applications.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"1636-1650"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452089","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}

引用次数: 0

Estimation of the Maximum Plane Positioning Error of Deep Submersibles Based on Underwater Virtual Reference Points 基于水下虚拟参考点的深潜器最大平面定位误差估计

IF 5.6 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2026-07-01 Epub Date: 2026-06-08 DOI: 10.1109/JOE.2026.3693857

Wenlong Gao;Yongfu Sun;Yibin Li;Muzi Zhang;Yue Zhang;Yixu Liu;Yinghao Zhuang;Yan Song

{"title":"Estimation of the Maximum Plane Positioning Error of Deep Submersibles Based on Underwater Virtual Reference Points","authors":"Wenlong Gao;Yongfu Sun;Yibin Li;Muzi Zhang;Yue Zhang;Yixu Liu;Yinghao Zhuang;Yan Song","doi":"10.1109/JOE.2026.3693857","DOIUrl":"https://doi.org/10.1109/JOE.2026.3693857","url":null,"abstract":"Integrated navigation is widely used for the navigation and positioning of submersibles. However, in deep-sea environments, the inability to obtain Global Navigation Satellite System signals and the lack of precise underwater reference points on the seabed make it challenging to quickly and cost-effectively estimate the maximum plane positioning error (MPPE) of the ultra short baseline (USBL)-assisted integrated navigation system at a 95<inline-formula><tex-math>$%$</tex-math></inline-formula> confidence level. This article proposes a method for estimating the MPPE of integrated navigation coordinates based on underwater virtual reference points. This method introduces underwater virtual reference points and derives analytical expressions for error bounds using vector representation and the Cauchy–Schwarz inequality, then calculates the MPPE for integrated navigation coordinates at a 95<inline-formula><tex-math>$%$</tex-math></inline-formula> confidence level by sorting the upper and lower bounds of horizontal error in ascending order. Experimental results indicate that for submersibles operating at an average slant range of 4201 m, the MPPE for strapdown inertial navigation system/USBL integrated navigation coordinates is 11.74 m, representing 0.76<inline-formula><tex-math>$%$</tex-math></inline-formula> of the submersible's range. The use of long baseline positioning values as ground truth validates the accuracy of these estimation results. The proposed method for estimating the MPPE of integrated navigation coordinates requires only speed, heading, attitude, and USBL positioning data, thereby eliminating the need for specialized positioning error estimation experiments. This offers a more convenient, rapid, and cost-effective solution. Estimating the MPPE provides significant reference for determining the submersible's positioning errors during marine environmental data collection and establishing the coordinate range of important underwater targets.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"2545-2554"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452132","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}

引用次数: 0

Experimental Investigation of the Optimal Position of Underwater Snake Robots for Energy Harvesting 水下蛇形机器人能量采集最佳位置的实验研究

IF 5.6 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2026-07-01 DOI: 10.1109/JOE.2026.3697338

Amer Orucevic;Mads Erlend Bøe Lysø;Ivan Gushkov;Henrik M. Schmidt-Didlaukies;Kristin Y. Pettersen;Jan Tommy Gravdahl

引用次数: 0

Adaptive Spectral Line Enhancement Using Deep Feature Extraction Guided FISTA-Net for Noise-Resilient Underwater Passive Sonar Detection and Classification 基于深度特征提取的FISTA-Net自适应谱线增强水下被动声纳检测与分类

IF 5.6 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2026-07-01 Epub Date: 2026-04-09 DOI: 10.1109/JOE.2026.3675821

Riya Rani S S;Sumit Datta

{"title":"Adaptive Spectral Line Enhancement Using Deep Feature Extraction Guided FISTA-Net for Noise-Resilient Underwater Passive Sonar Detection and Classification","authors":"Riya Rani S S;Sumit Datta","doi":"10.1109/JOE.2026.3675821","DOIUrl":"https://doi.org/10.1109/JOE.2026.3675821","url":null,"abstract":"The low frequency analysis and recording (LOFAR) spectrum plays a vital role in underwater passive sonar target detection. High noise levels in the marine environment degrade the quality of spectral lines in LOFAR spectrograms and hinder target detection. This article proposes a novel hybrid approach for LOFAR spectrum enhancement that integrates a sparse optimization algorithm and a deep learning approach for adaptive, noise resilient spectral line preservation. The proposed architecture employs U-shaped network (U-Net) as a spectral line selector and a cascaded fast iterative shrinkage-thresholding algorithm (FISTA) network for adaptive noise suppression. The U-Net selectively emphasizes salient frequency components, guiding the subsequent FISTA blocks to perform enhancement while preserving critical spectral lines. A signal-to-noise ratio (SNR) estimation module dynamically evaluates the noise level and adaptively determines the appropriate denoising level, enabling precise noise suppression. Experimental results using ShipsEar data sets demonstrate that the proposed method, namely, deep feature extraction guided FISTA-Net achieves an SNR gain of 3.37 dB over the state-of-the-art method, namely, fast block processed sparsity adaptive line enhancer (ALE) and 7.12 dB over sparsity induced ALE. In addition to the SNR improvement, the proposed method achieves an average signal-to-distortion ratio of 21.64 dB, which is 2.67 dB higher than the best performing existing method, further confirming its effectiveness in preserving signal quality while suppressing noise. For the evaluation of classification performance, a common residual neural network classifier was employed across all methods. The proposed approach achieves a classification accuracy of 96.95%, outperforming existing state-of-the-art techniques, confirming the proposed method's effectiveness in enhancing the detection and recognition of underwater acoustic targets.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"2274-2283"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452130","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}

引用次数: 0

CCCSIN: Color Compensation and Cross-Scale Semantic Information-Guided Network for Underwater Image Enhancement 彩色补偿和跨尺度语义信息引导的水下图像增强网络

IF 5.6 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2026-07-01 Epub Date: 2026-06-25 DOI: 10.1109/JOE.2026.3695780

Ning Wang;Tingkai Chen;Fang Dong

{"title":"CCCSIN: Color Compensation and Cross-Scale Semantic Information-Guided Network for Underwater Image Enhancement","authors":"Ning Wang;Tingkai Chen;Fang Dong","doi":"10.1109/JOE.2026.3695780","DOIUrl":"https://doi.org/10.1109/JOE.2026.3695780","url":null,"abstract":"In this article, to simultaneously address color distortion and content reconstruction pertaining to underwater image enhancement, a color compensation and cross-scale semantic information-guided network (CCCSIN) is devised. The multidense connection residual block (MCRB) with spatial and pixel attentions in a cascading pattern is developed, such that color distortion of underwater image can be effectively alleviated. By virtue of global and local semantic information derived from the semantic segmentation model, the cross-scale semantic feature enhancement module (CSFEM) is created, such that detailed image content can be accurately reconstructed. Employing the supervision of absolute error and mean square error loss functions, the entire CCCSIN scheme is constructed by organically combining with MCRB and CSFEM. Experiments demonstrate the effectiveness and superiority of the proposed CCCSIN scheme, peak signal-to-noise ratios are 3.2981, 2.2953, 2.1711, 5.4679, 7.3372, 9.5632, 4.1839, and 2.4253 higher than those of contrast limited adaptive histogram equalization, integrated colour model (ICM), unsupervised correction method (UCM), dark channel prior (DCP), image blurring and light absorption, underwater DCP, underwater light attenuation prior, and fast underwater image enhancement with generative adversarial networks (FunIE-GAN) frameworks, respectively, demonstrating compound advantages in detail recovery, texture preservation, color correction, contrast improvement, and clarity enhancement.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"2465-2477"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452163","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}

引用次数: 0