Self-organized underwater image enhancement

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2024, Volume and Issue: 215, P. 1 - 14

Published: June 29, 2024

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Underwater image captioning: Challenges, models, and datasets

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2025, Volume and Issue: 220, P. 440 - 453

Published: Jan. 5, 2025

Language: Английский

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Oil Spill Drift Prediction Enhanced by Correcting Numerically Forecasted Sea Surface Dynamic Fields with Adversarial Temporal Convolutional Networks

IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2025, Volume and Issue: 63, P. 1 - 18

Published: Jan. 1, 2025

Timely and accurate representation of sea surface dynamic fields is crucial for oil spill drift prediction. Numerically forecasted are available in a timely manner, but their accuracy limited. Conversely, reanalysis offer superior suffer from time delays. To enhance the performance prediction, we propose deep learning-based approach to correcting numerically fields, aligning them more closely with fields. Our introduces an adversarial temporal convolutional network (ATCN) framework, consisting (TCN)-based corrector discriminator. The TCN can characterize field sequences both spatially temporally. In this scenario, processes outputs corrected that approximate Adversarial training discriminator further refines corrector. This enhances prediction using We also provide dataset drifts Symphony Sanchi accidents, including related data remote sensing data, establishing baseline evaluating Experiments on validate ATCN framework's effectiveness enhancing

Language: Английский

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Cascaded frameworks in underwater optical image restoration

Information Fusion, Journal Year: 2024, Volume and Issue: 117, P. 102809 - 102809

Published: Nov. 30, 2024

Language: Английский

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Marine Equipment Siting Using Machine-Learning-Based Ocean Remote Sensing Data: Current Status and Future Prospects

Sustainability, Journal Year: 2024, Volume and Issue: 16(20), P. 8889 - 8889

Published: Oct. 14, 2024

As the global climate changes, there is an increasing focus on oceans and their protection exploitation. However, exploration of necessitates construction marine equipment, siting such equipment has become a significant challenge. With ongoing development computers, machine learning using remote sensing data proven to be effective solution this problem. This paper reviews history technology, introduces conditions required for site selection through measurement analysis, uses cluster analysis methods identify areas as research hotspot ocean sensing. The aims integrate into Through review discussion article, limitations shortcomings current stage are identified, relevant proposals put forward.

Language: Английский

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Region gradient-guided diffusion model for underwater image enhancement

Machine Vision and Applications, Journal Year: 2025, Volume and Issue: 36(2)

Published: Jan. 17, 2025

Language: Английский

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Underwater image enhancement using generative adversarial networks: a survey

International Journal of Computers and Applications, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 17

Published: Feb. 20, 2025

Language: Английский

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A real-time recognition and distance measurement method for underwater dynamic obstacles based on binocular vision

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 117329 - 117329

Published: March 1, 2025

Language: Английский

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High-Precision 3D Reconstruction in Complex Scenes via Implicit Surface Reconstruction Enhanced by Multi-Sensor Data Fusion

Sensors, Journal Year: 2025, Volume and Issue: 25(9), P. 2820 - 2820

Published: April 30, 2025

In this paper, we investigate implicit surface reconstruction methods based on deep learning, enhanced by multi-sensor data fusion, to improve the accuracy of 3D in complex scenes. Existing single-sensor approaches often struggle with occlusions and incomplete observations. By fusing complementary information from multiple sensors (e.g., cameras or a combination depth sensors), our proposed framework alleviates issue missing partial further increases fidelity. We introduce novel neural network that learns continuous signed distance function (SDF) for scene geometry, conditioned fused feature representations. The seamlessly merges multi-modal into unified representation, enabling precise watertight reconstruction. conduct extensive experiments datasets, demonstrating superior compared baselines classical fusion methods. Quantitative qualitative results reveal significantly improves completeness geometric detail, while approach provides smooth, high-resolution surfaces. Additionally, analyze influence number diversity quality, model’s ability generalize unseen data, computational considerations. Our work highlights potential coupling representations achieve robust challenging real-world conditions.

Language: Английский

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Joint Luminance-Saliency Prior and Attention for Underwater Image Quality Assessment

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(16), P. 3021 - 3021

Published: Aug. 17, 2024

Underwater images, as a crucial medium for storing ocean information in underwater sensors, play vital role various tasks. However, they are prone to distortion due the imaging environment, which leads decline visual quality, is an urgent issue marine vision systems address. Therefore, it necessary develop image enhancement (UIE) and corresponding quality assessment methods. At present, most (UIQA) methods primarily rely on extracting handcrafted features that characterize degradation attributes, struggle measure complex mixed distortions often exhibit discrepancies with human perception practical applications. Furthermore, current UIQA lack consideration of perspective enhanced effects. To this end, paper employs luminance saliency priors critical first time effect global local achieved by UIE algorithms, named JLSAU. The proposed JLSAU built upon overall pyramid-structured backbone, supplemented Luminance Feature Extraction Module (LFEM) Saliency Weight Learning (SWLM), aim at obtaining multiple scales. supplement aims perceive visually sensitive luminance, including histogram statistical grayscale positional information. reflects variation both spatial channel domains. Finally, effectively model relationship among different levels contained multi-scale features, Attention Fusion (AFFM) proposed. Experimental results public UIQE UWIQA datasets demonstrate outperforms existing state-of-the-art

Language: Английский

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