Search Results (21)

Underwater image enhancement is crucial for fields like marine exploration, underwater photography, and environmental monitoring, as underwater images often suffer from reduced visibility, color distortion, and contrast loss due to light absorption and scattering. Despite recent progress, existing methods struggle to generalize across diverse underwater conditions, such as varying turbidity levels and lighting. This paper proposes a novel hybrid UNet–Transformer architecture based on MaxViT blocks, which effectively combines local feature extraction with global contextual modeling to address challenges including low contrast, color distortion, and detail degradation. Extensive experiments on two benchmark datasets, UIEB and EUVP, demonstrate the superior performance of our method. On UIEB, our model achieves a PSNR of 22.91, SSIM of 0.9020, and CCF of 37.93—surpassing prior methods such as URSCT-SESR and PhISH-Net. On EUVP, it attains a PSNR of 26.12 and PCQI of 1.1203, outperforming several state-of-the-art baselines in both visual fidelity and perceptual quality. These results validate the effectiveness and robustness of our approach under complex underwater degradation, offering a reliable solution for real-world underwater image enhancement tasks. Full article

This work studies the water-exit problems of slender cylinders under various conditions through experimental investigation. An experimental platform was equipped with high-speed photography. A total of 13 experimental cases with varying head shapes (conical, spherical, and truncated cone designs), length-to-diameter ratios (5:1–7:1), ejection velocities (7.24–17.93 m/s), and elastic moduli (227.36–279.14 MPa) were conducted to capture water-exit characteristics. The investigation identified ejection velocity as the predominant parameter governing cavity morphology and stability, with higher velocities correlating to increased cavity dimensions and reduced drag coefficients by 54%. Conical head shape resulted in superior drag reduction characteristics, forming a typical cigar-shaped cavity with clear and regular boundaries. Additionally, an increased length-to-diameter ratio substantially improved drag reduction performance by 33%. Material elastic moduli proved crucial for water-exit stability, as cylinders with lower moduli experienced severe bending deformation and even trajectory changes, while higher moduli cylinders maintained their form with minimal deformation. This study illuminates the physical mechanisms of slender body water-exit under multi-factor coupling conditions, providing experimental evidence and theoretical guidance for cross-media vehicle design and underwater equipment optimization. Full article

The paper introduces a novel method for creating a photographic map of the seabed using images captured by the on-board photo and video systems of autonomous underwater vehicles (AUVs) during various missions, while incorporating navigation parameters. Additionally, it presents a new approach for storing this photo map on the on-board device in a mosaic format (tiles), which significantly accelerates operational visual inspection by enabling the automatic search and recognition of underwater objects that may exceed the coverage area of a single photograph. This capability is achieved by organizing the photo map into layers with varying zoom levels. Semi-natural experiments were conducted with data from actual missions using the real underwater vehicle demonstrate the high efficiency of the proposed method and algorithm. Unlike existing methods that form photo maps after the underwater vehicle has taken pictures of the bottom using special high-performance computers, the developed method forms a photo map directly during the movement of the vehicle, using only the computing power of the on-board computer. In addition, in the event of accidents, when it is necessary to detect objects of interest on the seabed as quickly as possible, it is necessary to provide a quick visual inspection of the generated photo map. For this purpose, we have developed an algorithm for saving a photo map in the form of a mosaic, which is widely used in interactive geographic maps, such as Google Maps. This algorithm differs from existing methods in that it selectively saves data to the on-board storage device to reduce the number of read and write operations, thus ensuring the timely operation of the entire process of creating a photo map at a given frequency of photography. After the generated map has been stored as a mosaic and a high-speed connection with the vehicle has appeared, the operator can immediately view the entire generated map using a regular web browser. Full article

For almost all the Sicilian islands, there are no faunistic data concerning marine Heterobranchia, which is one of the most sought-after groups of marine critters by photographers and diving enthusiasts all over the world. With the present study, carried out through underwater photography at various dive sites and stretches of coastline in the island of Marettimo, we made the first contribution to the knowledge of the marine Heterobranchia fauna present on this island of the Egadi archipelago. Through data collection, it was possible to document the presence of 43 species of marine Heterobranchia. Data analysis showed a remarkable homogeneity in the number of species between the examined sites. This is probably due to the peculiar environmental homogeneity present in the sites of this island, which are almost all rich in the presence of both benthic suspension feeders (the favorite prey of many groups of marine Heterobranchia) and environments full of crevices, grottos, and vertical walls, which are the preferred habitats of the majority of these mollusks. The higher number of marine heterobranch species found in Marettimo compared to the smaller number of species found on the other recently examined Sicilian islands (Pantelleria, Lipari, and Vulcano) is probably due to the massive presence of rich coralligenous biocoenoses and the particular hydrodynamic regime to which Marettimo is subject. Full article

This paper presents a novel underwater image enhancement method addressing the challenges of low contrast, color distortion, and detail loss prevalent in underwater photography. Unlike existing methods that may introduce color bias or blur during enhancement, our approach leverages a two-pronged strategy. First, an Efficient Fusion Edge Detection (EFED) module preserves crucial edge information, ensuring detail clarity even in challenging turbidity and illumination conditions. Second, a Multi-scale Color Parallel Frequency-division Attention (MCPFA) module integrates multi-color space data with edge information. This module dynamically weights features based on their frequency domain positions, prioritizing high-frequency details and areas affected by light attenuation. Our method further incorporates a dual multi-color space structural loss function, optimizing the performance of the network across RGB, Lab, and HSV color spaces. This approach enhances structural alignment and minimizes color distortion, edge artifacts, and detail loss often observed in existing techniques. Comprehensive quantitative and qualitative evaluations using both full-reference and no-reference image quality metrics demonstrate that our proposed method effectively suppresses scattering noise, corrects color deviations, and significantly enhances image details. In terms of objective evaluation metrics, our method achieves the best performance in the test dataset of EUVP with a PSNR of 23.45, SSIM of 0.821, and UIQM of 3.211, indicating that it outperforms state-of-the-art methods in improving image quality. Full article

During underwater image processing, image quality is affected by the absorption and scattering of light in water, thus causing problems such as blurring and noise. As a result, poor image quality is unavoidable. To achieve overall satisfying research results, underwater image denoising is vital. This paper presents an underwater image denoising method, named HHDNet, designed to address noise issues arising from environmental interference and technical limitations during underwater robot photography. The method leverages a dual-branch network architecture to handle both high and low frequencies, incorporating a hybrid attention module specifically designed for the removal of high-frequency abrupt noise in underwater images. Input images are decomposed into high-frequency and low-frequency components using a Gaussian kernel. For the high-frequency part, a Global Context Extractor (GCE) module with a hybrid attention mechanism focuses on removing high-frequency abrupt signals by capturing local details and global dependencies simultaneously. For the low-frequency part, efficient residual convolutional units are used in consideration of less noise information. Experimental results demonstrate that HHDNet effectively achieves underwater image denoising tasks, surpassing other existing methods not only in denoising effectiveness but also in maintaining computational efficiency, and thus HHDNet provides more flexibility in underwater image noise removal. Full article

To improve detection efficiency and reduce cost consumption in fishery surveys, target detection methods based on computer vision have become a new method for fishery resource surveys. However, the specialty and complexity of underwater photography result in low detection accuracy, limiting its use in fishery resource surveys. To solve these problems, this study proposed an accurate method named BSSFISH-YOLOv8 for fish detection in natural underwater environments. First, replacing the original convolutional module with the SPD-Conv module allows the model to lose less fine-grained information. Next, the backbone network is supplemented with a dynamic sparse attention technique, BiFormer, which enhances the model’s attention to crucial information in the input features while also optimizing detection efficiency. Finally, adding a 160 × 160 small target detection layer (STDL) improves sensitivity for smaller targets. The model scored 88.3% and 58.3% in the two indicators of mAP@50 and mAP@50:95, respectively, which is 2.0% and 3.3% higher than the YOLOv8n model. The results of this research can be applied to fishery resource surveys, reducing measurement costs, improving detection efficiency, and bringing environmental and economic benefits. Full article

Mapping benthic habitats with bathymetric, acoustic, and spectral data requires georeferenced ground-truth information about habitat types and characteristics. New technologies like autonomous underwater vehicles (AUVs) collect tens of thousands of images per mission making image-based ground truthing particularly attractive. Two types of machine learning (ML) models, random forest (RF) and deep neural network (DNN), were tested to determine whether ML models could serve as an accurate substitute for manual classification of AUV images for substrate type interpretation. RF models were trained to predict substrate class as a function of texture, edge, and intensity metrics (i.e., features) calculated for each image. Models were tested using a manually classified image dataset with 9-, 6-, and 2-class schemes based on the Coastal and Marine Ecological Classification Standard (CMECS). Results suggest that both RF and DNN models achieve comparable accuracies, with the 9-class models being least accurate (~73–78%) and the 2-class models being the most accurate (~95–96%). However, the DNN models were more efficient to train and apply because they did not require feature estimation before training or classification. Integrating ML models into benthic habitat mapping process can improve our ability to efficiently and accurately ground-truth large areas of benthic habitat using AUV or similar images. Full article

In order to address the challenges of small and micro-water pollution in parks and the low level of 3D visualization of water quality monitoring systems, this research paper proposes a novel wireless remote water quality monitoring system that combines the Internet of Things (IoT) and a 3D model of reality. To begin with, the construction of a comprehensive 3D model relies on various technologies, including unmanned aerial vehicle (UAV) tilt photography, 3D laser scanning, unmanned ship measurement, and close-range photogrammetry. These techniques are utilized to capture the park’s geographical terrain, natural resources, and ecological environment, which are then integrated into the three-dimensional model. Secondly, GNSS positioning, multi-source water quality sensors, NB-IoT wireless communication, and video surveillance are combined with IoT technologies to enable wireless remote real-time monitoring of small and micro-water bodies. Finally, a high-precision underwater, indoor, and outdoor full-space real-scene three-dimensional visual water quality monitoring system integrated with IoT is constructed. The integrated system significantly reduces water pollution in small and micro-water bodies and optimizes the water quality monitoring system. Full article

Nanomaterials with unique structural and properties can be synthesized by rapid transition of the thermodynamic state. One promising method is through electrical explosion, which possesses ultrafast heating/quenching rates (dT/dt~10⁹ K/s) of the exploding conductor. In this study, experiments were performed with fine metallic wire exploding in liquid nitrogen (liq N2, 77 K) under different applied voltages. For the first time in the literature, the physical image of the electrical explosion dynamics in liq N2 is depicted using electro-physical diagnostics and spatial-temporal-resolved photography. Specifically, the pulsation and collapse processes of the vapor bubble (explosion products) have been carefully observed and analyzed. As a comparison, an underwater electrical explosion was also performed. The experimental results suggest that the vapor bubble behavior in liq N2 differs from that in water, especially in the collapse phase, characterized by secondary small-scale bubbles in liq N2, but multiple bubble pulses in water; correspondingly, the products’ characteristics are discrepant. Full article

This paper presents an efficient underwater image enhancement method, named ECO-GAN, to address the challenges of color distortion, low contrast, and motion blur in underwater robot photography. The proposed method is built upon a preprocessing framework using a generative adversarial network. ECO-GAN incorporates a convolutional neural network that specifically targets three underwater issues: motion blur, low brightness, and color deviation. To optimize computation and inference speed, an encoder is employed to extract features, whereas different enhancement tasks are handled by dedicated decoders. Moreover, ECO-GAN employs cross-stage fusion modules between the decoders to strengthen the connection and enhance the quality of output images. The model is trained using supervised learning with paired datasets, enabling blind image enhancement without additional physical knowledge or prior information. Experimental results demonstrate that ECO-GAN effectively achieves denoising, deblurring, and color deviation removal simultaneously. Compared with methods relying on individual modules or simple combinations of multiple modules, our proposed method achieves superior underwater image enhancement and offers the flexibility for expansion into multiple underwater image enhancement functions. Full article

Underwater imaging has been present for many decades due to its relevance in vision and navigation systems. In recent years, advances in robotics have led to the availability of autonomous or unmanned underwater vehicles (AUVs, UUVs). Despite the rapid development of new studies and promising algorithms in this field, there is currently a lack of research toward standardized, general-approach proposals. This issue has been stated in the literature as a limiting factor to be addressed in the future. The key starting point of this work is to identify a synergistic effect between professional photography and scientific fields by analyzing image acquisition issues. Subsequently, we discuss underwater image enhancement and quality assessment, image mosaicking and algorithmic concerns as the last processing step. In this line, statistics about 120 AUV articles fro recent decades have been analyzed, with a special focus on state-of-the-art papers from recent years. Therefore, the aim of this paper is to identify critical issues in autonomous underwater vehicles encompassing the entire process, starting from optical issues in image sensing and ending with some issues related to algorithmic processing. In addition, a global underwater workflow is proposed, extracting future requirements, outcome effects and new perspectives in this context. Full article

Monitoring and evaluating the biofouling status of a ship’s hull and its effects on the vessel’s performance attracts the attention of both researchers and industry. In this study, two types of monitoring equipment were used to observe organism growth on two fishing vessels for approximately six months. Combining underwater photography technology with periodic cleaning methods can effectively prevent the occurrence of problems including hull biofouling. The monitoring system developed in this study is cheap and easy to operate, and can be stored on board and regularly operated by the crew to eliminate various issues below the waterline, which in turn enhances sustainable shipping. Full article

Underwater sampling needs to strike a balance between time-efficient and standardized data that allow comparison with different areas and times. The roving diver survey involves divers meandering and actively searching for species and has been useful for producing fish species lists but has seldom been implemented for benthic taxa. In this study, we used this non-destructive technique to register species associated with kelp forests at the sub-Antarctic Bécasses Island (Beagle Channel, Argentina), detecting numerous species while providing the first multi-taxa inventory for the area, including macroalgae, invertebrates, and fish, with supporting photographs of each observation hosted on the citizen science platform iNaturalist. This research established a timely and cost-effective methodology for surveys with scuba diving in cold waters, promoting the obtention of new records, data sharing, and transparency of the taxonomic curation. Overall, 160 taxa were found, including 41 not reported previously for this area and three records of southernmost distribution. Other studies in nearby areas with extensive sampling efforts arrived at similar richness estimations. Our findings reveal that the roving diver survey using photographs is a good approach for creating inventories of marine species, which will serve for a better understanding of underwater biodiversity and future long-term monitoring to assess the health of kelp environments. Full article

When an airgun releases high-pressure gas underwater below an ice plate, it is observed that a bubble is formed rapidly while the ice plate is broken fiercely. In order to study the ice-water-gas interaction during this transient and violent phenomenon, a set of laboratory-scale devices was designed and a series of icebreaking experiments were carried out. High-speed photography was used to capture the evolution of the bubble and the ice plate. It was found that the airgun bubble had a unique ‘pear’ shape compared with the spherical bubble generated by electric sparking. The pressure induced by the pulsation of the airgun bubble near a rigid wall was measured by the pressure sensor. The initial shockwave, oscillatory pressure peaks caused by the directional fast air injection, secondary shockwave, and pressure peak caused by the bubble jet impact were clearly recorded. Three damage patterns of ice plates were observed and corresponding reasons were analyzed. The influence of dimensionless parameters, such as airgun-ice distance $H$ and ice thickness $T$, was also investigated. The physical mechanism of ice-water-gas interaction was summarized. Full article