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![FIGURE 16. Results of CGI. NTU: the unit of turbidity, fs: frames [82]](publication/352243372/figure/fig4/AS:1032621127639044@1623207470287/Results-of-CGI-NTU-the-unit-of-turbidity-fs-frames-82_Q320.jpg)
The challenges associated with acquiring the clear images of objects in underwater environment are difficult to overcome due to the absorptive and scattering nature of seawater. Recently, the research community has focused on mitigating these effects. The recent developments in image enhancement algorithms and strategies of signal light enhancement...
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... applications of underwater optical imaging include airborne sea surface detectors, shipborne underwater optical detectors, and submarine-borne optical cameras. This is presented in Fig. 1. As compared with the atmospheric imaging technologies, the underwater imaging technologies focus on reducing the impact of attenuation, such as strong absorption and scattering of light caused by water on the quality of the acquired underwater images. Especially in turbid water, the visibility is significantly less as compared to the ...
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... from traditional underwater imaging, a polarized imaging system sends a beam of polarized light for the illumination and then receives echo signal with polarized information, as shown in Fig. 10. Generally speaking, underwater targets have rough surfaces, thus they have obvious depolarization effects. When a beam of polarized light illuminates the underwater scene, the signal light from target is unpolarized, while the backscattered light is partially polarized. A polarization imaging system can separate the signal light from ...
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... source to minimize the scattering during propagation, and decoded the polarization information of the target scene through optical correlation principle, which alleviated the image degradation due to the strong scattering of turbid water, and then the polarized images of targets in highly turbid water were recovered. This is presented in Fig. ...
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... hyperspectral imaging (UHI) system consists of three main components: hyperspectral imager, light source, and sensors, as shown in Figure 13. The underwater hyperspectral imager is the core component, which is used to acquire narrow-band spectral images of underwater targets in specific wavelength bands; the light source is used to provide illumination for underwater targets; sensors are used to correct the attenuation of light energy by the water. ...
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... In the same year, Liu et al. [76] developed a tunable LED-based underwater multispectral imaging system (TuLUMIS), which separated images with different wavebands by switching the light source band. The detection band range, the number of bands, and the spectral resolution of the TuLUMIS are 400-700 nm, 8, 10-100 nm respectively. As presented in Fig. 14, the monochrome images including eight spectral bands are presented on the left, and the fused pseudo-color image is shown on the right. Compared with the traditional underwater imaging system with RGB camera, the TuLUMIS improved the color discrimination by 76.66%. In 2020, Song et al. [77] developed a staring type underwater ...
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... in biomedical imaging, remote optical sensing, and underwater imaging [24]. Generally, ghost imaging employs pseudothermal light as the light source. According to the different ways of pseudothermal light generation, it can be divided into conventional ghost imaging (GI) and computational ghost imaging (CGI) [80]. The schematic of GI is shown in Fig. 15(a), a pseudothermal beam is generated by a rotating diffuser which is illuminated by a laser. The beam is then divided into two parts by the beam-splitter: one of the beams is the signal beam, which is transmitted (or reflected) by the object and collected by the bucket detector; the other is a reference beam, which is directly received ...
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... correlating the intensity measured by these two paths, the target image can be reconstructed. Different from GI, CGI generates pseudothermal light by loading a random grayscale map ( ) , r xy on the SLM, as shown in Fig.15(b). Meanwhile, the intensity distribution reflected from the SLM can be calculated according to the scalar diffraction theory, thus the beam splitter and the multi-pixel detector are not required to record the reference light field, and the system is greatly simplified. ...
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... and verified its feasibility in turbid water and long-range underwater imaging. In 2017, Le et al. [82] investigated an underwater computational ghost imaging (CGI) at different turbidities and angles. The quality of traditional underwater imaging decreases with increasing turbidity, while CGI shows strong robustness for turbidity. As shown in Fig. 16, even in highly turbid water, CGI can still acquire target image as long as sufficient sampling frames are obtained. And the more sampling frames, the higher SNR of the imaging. Moreover, the CGI allows images to be acquired from a wide AOV, which makes it adaptable to harsh underwater environments. In 2019, Zhang et al. [83] ...
Citations
... Traditional computer vision methods encounter challenges in these automatic tasks when confronted with complex and murky underwater scenes and targets [7]. Compared to analogous tasks conducted on terrestrial surfaces, underwater optical imaging in natural aquatic environments is subject to quality degradation due to light absorption and scattering phenomena [8]. They manifest as color biases, reduced contrast, blurred target edges, diminished detail, and overall decreased image clarity. ...
Semantic segmentation of targets in underwater images within turbid water environments presents significant challenges, hindered by factors such as environmental variability, difficulties in acquiring datasets, imprecise data annotation, and the poor robustness of conventional methods. This paper addresses this issue by proposing a novel joint method using deep learning to effectively perform semantic segmentation tasks in turbid environments, with the practical case of efficiently collecting polymetallic nodules in deep-sea while minimizing damage to the seabed environment. Our approach includes a novel data expansion technique and a modified U-net based model. Drawing on the underwater image formation model, we introduce noise to clear water images to simulate images captured under varying degrees of turbidity, thus providing an alternative to the required data. Furthermore, traditional U-net-based modified models have shown limitations in enhancing performance in such tasks. Based on the primary factors underlying image degradation, we propose a new model which incorporates an improved dual-channel encoder. Our method significantly advances the fine segmentation of underwater images in turbid media, and experimental validation demonstrates its effectiveness and superiority under different turbidity conditions. The study provides new technical means for deep-sea resource development, holding broad application prospects and scientific value.
... particularly those that require continuous positioning and navigation capabilities. They are commonly used in ROVs and AUVs for underwater inspection and maintenance tasks. Optical imaging systems are primarily used for underwater surveying and mapping applications, where high-resolution images of the seabed and underwater structures are required (Shen, et. al., 2021. ...
Subsea navigation technologies play a critical role in enhancing precision and efficiency in offshore engineering projects. This comparative review examines the evolution and application of subsea navigation systems, with a focus on improving precision in offshore construction and surveying. The study evaluates various subsea navigation technologies, including acoustic positioning systems, inertial navigation systems, and optical imaging systems, highlighting their capabilities and limitations. The review begins by discussing the historical development of subsea navigation technologies, tracing their evolution from early acoustic systems to modern integrated solutions. It then explores the key components and operation principles of each technology, providing insights into their functionalities and suitability for different offshore applications. The study also examines the challenges and advancements in subsea navigation, including issues related to signal interference, accuracy, and real-time data processing. It discusses how these challenges have been addressed through technological innovations, such as improved sensor technologies and advanced data fusion algorithms. Furthermore, the review assesses the practical application of subsea navigation technologies in offshore engineering projects, including pipeline installation, subsea infrastructure maintenance, and underwater surveying. It analyzes case studies and industry practices to illustrate the effectiveness of these technologies in improving precision, reducing costs, and mitigating risks in offshore operations. Overall, this comparative review provides a comprehensive overview of subsea navigation technologies in offshore engineering projects. It highlights the evolution, capabilities, and applications of these technologies, emphasizing their role in enhancing precision and efficiency in offshore construction and surveying. The study concludes with recommendations for future research and development to further improve the performance and reliability of subsea navigation systems in offshore operations.
... Technological advancements have enabled humans to venture deeper into the oceans, increasing the demand for high-quality underwater imagery, crucial for scientific research, environmental monitoring, and commercial applications [1]. However, capturing clear and detailed images in underwater environments is inherently challenging due to the physical properties of water impacting light transmission and scattering [2]. As light travels through water, it undergoes attenuation, resulting in intensity decrease and altering the color spectrum. ...
Underwater imagery plays a pivotal role in various scientific, industrial, and recreational applications, ranging from marine biology and oceanography to underwater archaeology and resource exploration. However, capturing high-quality images in underwater environments poses unique challenges due to factors such as light attenuation, color distortion, and particulate matter suspended in the water. In recent years, significant advancements have been made in the development of advanced techniques for enhancing underwater imagery aimed at improving perceptual quality, sharpness, and detail preservation. This research explores state-of-the-art enhancement techniques, focusing on multi-channel histogram equalization and depth-adaptive correction in the context of deep reinforcement learning (DRL) and variational autoencoders (VAE). The research presents a comprehensive analysis of the performance of these techniques using numerical metrics such as perceptual quality, sharpness, and detail preservation, derived from experimental evaluations. The findings demonstrate the effectiveness of VAE in enhancing perceptual quality and sharpness, with underwater image dehazing exhibiting commendable results as well. Furthermore, Convolutional Neural Networks (CNN) emerge as a promising approach for detail preservation in underwater images. By advancing the capabilities of underwater image enhancement techniques, this research contributes to the broader goal of unlocking the full potential of underwater exploration and understanding. The findings reveal significant improvements in image quality achieved by these techniques. VAE exhibits the highest perceptual quality scores, averaging around 0.84, with sharpness scores reaching approximately 0.91.
... High-quality underwater images can help developers better explore the underwater world, so underwater image processing has become a promising research area. However, due to the complexity of the underwater environment and the absorption and scattering effects of light, the quality of underwater images collected by underwater cameras is seriously degraded (Shen et al. 2021). Specifically, the quality of underwater images can be impacted by water turbidity and light scattering, resulting in blurry, low contrast, and unrealistic color (Wang et al. 2019;Soni and Kumare 2020). ...
Underwater images are affected by light scattering and absorption in the water. It usually faces challenges such as color distortion, loss of details, and low contrast. To address the above problems, this paper proposes a dual-color space color correction and histogram segmentation optimized strategy for underwater image enhancement. Specifically, this paper first calculates the quantile for adjusting the pixel distribution and performs histogram stretching to correct the image’s overall color. Then, a LAB color balancing strategy is designed to eliminate the color deviation resulting from the color correction process. Finally, histogram segmentation and adaptive pixel allocation methods are proposed to improve overall contrast. Experimental studies on three benchmark datasets for comparison with six state-of-the-art algorithms are conducted. Experimental results show the effectiveness of the mechanism proposed in this paper. Meanwhile, the proposed approach proves effective for key point and saliency detection. Additionally, the proposed approach exhibits promising results for images captured under challenging conditions such as low illumination, haze, and dust storms.
... Underwater imaging technology is the basis for understanding and developing the ocean, optical imaging is currently the primary technological way of underwater imaging [1,2], which is widely used in underwater exploration. Underwater optical imaging still faces considerable challenges [3]. ...
... Typically, in underwater applications, curved windows are predominantly of the domed variety, featuring front and rear surfaces that conform to concentric spheres. Specifically, the thickness is 1 2 l d r r = − . The concentric configuration offers the dual benefit of withstanding pressure while ensuring a consistent field of view size [10]. ...
When photographing objects underwater, it is important to utilize an optical window to isolate the imaging device from the water. The properties of the entire imaging system will change, and the imaging quality will decrease due to the refraction impact of the water and the window. The theoretical calculation method for air imaging is no longer relevant in this context. To analyze the unique rule, this research derives the formulas for key parameters of underwater imaging systems under paraxial circumstances. First, the optical window is modeled, then the formula for the optical window’s focal length in the underwater environment is derived, and the change rule for the focal length of various window forms underwater is condensed. For the ideal imaging system using a domed optical window, the equivalent two-optical group model of the imaging system is established, and the formula for calculating the focal length, working distance, and depth of field of the underwater imaging system is derived through paraxial ray tracing. The accuracy of the formula is verified through the comparative analysis of the formula calculation results and the Zemax modeling simulation results. It provides an important theoretical basis for the in-depth study of underwater imaging technology.
... In the industrial sector, this technology finds extensive applications in several areas, such as industrial product quality inspection, building analysis [5], water conservancy troubleshooting, and other related sectors [6]. Marine scientific research uses technology to investigate various aspects of the ocean, including the exploration of oil and gas resources beneath the seabed, mapping the topography of the seabed, and the search for submerged archaeological structures of historical significance [7]. Furthermore, due to the advancements and practical implementation of contemporary electronic information technology and underwater imaging technology, the latter is utilized to detect reefs and beacon structures, among other things, and is, thus, employed to some degree in maritime vessel navigation [8]. ...
Laser scanning 3D imaging technology, because it can obtain accurate three-dimensional surface data, has been widely used in the search for wrecks and rescue operations, underwater resource development, and other fields. At present, the conventional underwater spinning laser scanning imaging system maintains a relatively fixed light window. However, in low-light situations underwater, the rotation of the scanning device causes some degree of water fluctuation, which warps the light strip data that the system sensor receives about the object’s surface. To solve this problem, this research studies an underwater 3D scanning and imaging system that makes use of a fixed light window and a spinning laser (FWLS). A refraction error compensation algorithm is investigated that is based on the fundamentals of linear laser scanning imaging, and a dynamic refraction mathematical model is established based on the motion of the imaging device. The results of the experiment on error analysis in an optimal underwater environment indicate that the error in reconstructing the radius is decreased by 60% (from 2.5 mm to around 1 mm) when compensating for the measurement data of a standard sphere with a radius of 20 mm. Moreover, the compensated point cloud data exhibit a higher degree of correspondence with the model of the standard spherical point cloud. Furthermore, we examine the impact of physical noise, measurement distance, and partial occlusion of the object on the imaging system inside an authentic underwater setting. This study is a good starting point for looking at the refractive error of an underwater laser scanning imaging system. It also provides to us some ideas for future research on the refractive error of other scanning imaging methods.
... In the industrial sector, this technology finds extensive applications in several areas, such as industrial product quality inspection, engineering mapping, building analysis, water conservancy troubleshooting, path planning, and other related sectors. Marine scientific research uses technology to investigate various aspects of the ocean, including the exploration of oil and gas resources beneath 2 the seabed, mapping the topography of the seabed, and the search for submerged archaeological structures of historical significance [2][3][4][5]. ...
Laser scanning 3D imaging technology, because it can get accurate three-dimensional surface data, has been widely used in the search for wrecks and rescue operations, underwater resource development, and other fields. At present, the conventional underwater rotating laser scanning imaging system maintains a relatively fixed light window. However, in low-light situations underwater, the rotation of the scanning device causes some degree of water fluctuation, which warps the light strip data that the system sensor receives about the object's surface. To solve the problem, this research studies an underwater 3D scanning and imaging system that makes use of a fixed-light window and a spinning laser (FWLS). A refraction error compensation algorithm is investigated that is based on the fundamentals of linear laser scanning imaging and the dynamic refraction mathematical model is established by the motion of the imaging device. During the imaging process, the incident angle between the laser and the light window varies due to the scanning mode of the system. The experimental results show that the reconstruction radius error is reduced by 60% (from 2.5 mm to about 1 mm) when the measurement data for a standard sphere with a radius of 20 mm are compensated. Moreover, the compensated point cloud data exhibits a higher degree of correspondence with the model of the standard spherical point cloud. This study has a specific reference value for the refractive error analysis of an underwater laser scanning imaging system, and it provides certain research ideas for the subsequent refractive error analysis of various scanning imaging modalities.
... In recent years, there has been growing recognition of the scientific significance, economic benefits, and strategic importance of ocean surveys, which has contributed to the accelerated development of technologies, such as underwater sensor networks [1] and underwater vehicles [2]. Therefore, there is an increasing demand for advanced underwater detection technology to support marine survey activities. ...
... These limitations affect the performance of underwater Lidar systems, restricting their maximal detectable range and accuracy. Consequently, scientists have investigated various techniques to improve the performance of underwater detection, such as selecting an optimal laser source [14], photon counting [15], polarization detection [2], and range-gating [16], etc. Among these technologies, the range-gated approach has become a primary means of achieving long-range target detection in the underwater environment, which filtered out near-field backscattering by adjusting the delay between the detector's gate open time and the emitted laser pulse. ...
The efficacy of underwater laser detection is considerably impacted by the intense attenuation of light resulting from the scattering and absorption effects of water. In this study, we present the simulation and design of the underwater Lidar system that integrates the paraxial multi-channel detection strategy to enhance the dynamic range in subsea environments. To evaluate the performance of the system with multiple detection channels, we introduce a multi-channel underwater Lidar simulation (MULS) method based on the radiative transfer Lidar equations. Experimental validations were conducted under varied water conditions to assess the performance of the prototype and validate the simulation results. The measured range accuracy of each channel in the prototype is better than 0.1085 m, and the simulated and measured waveforms exhibit strong correlations, verifying the reliability and validity of the simulation method. The effects of transceiver configuration and the maximum detectable range of different detection methods were also discussed. Preliminary results indicate that the paraxial multi-channel design effectively suppresses near-field backscattering and substantially enhances the maximum detectable range. The findings presented in this study may provide valuable insights for the design and optimization of future underwater laser detection systems.
... The Underwater Optical Imaging (UOI) model can obtain natural and clear underwater images by establishing a rough optical imaging model and reversing the degradation process [58]. This model is defined by Equation 2. There are many underwater optical imaging applications, such as detectors for onboard underwater optics, aerial, ocean-surface, and underwater optical cameras [59]. ...
In recent years, underwater exploration for deep-sea resource utilization and development has a considerable interest. In an underwater environment, the obtained images and videos undergo several types of quality degradation resulting from light absorption and scattering, low contrast, color deviation, blurred details, and nonuniform illumination. Therefore, the restoration and enhancement of degraded images and videos are critical. Numerous techniques of image processing, pattern recognition and computer vision have been proposed for image restoration and enhancement, but many challenges remain. This survey presents a comparison of the most prominent approaches in underwater image processing and analysis. It also discusses an overview of the underwater environment with a broad classification into enhancement and restoration techniques and introduces the main underwater image degradation reasons in addition to the underwater image model. The existing underwater image analysis techniques, methods, datasets, and evaluation metrics are presented in detail. Furthermore, the existing limitations are analyzed, which are classified into image-related and environment-related categories. In addition, the performance is validated on images from the UIEB dataset for qualitative, quantitative, and computational time assessment. Areas in which underwater images have recently been applied are briefly discussed. Finally, recommendations for future research are provided and the conclusion is presented.
... The propagation of light in underwater environments is impeded by the high absorption and scattering of light waves [1], which restricts the effective range of optical-based visual detection to short distances. This limitation necessitates the use of alternative sensing modalities, such as acoustic imaging, which offers a long detection range due to the negligible effects of water and dissolved impurities on the propagation of sound waves. ...
Segment Anything Model (SAM) has revolutionized the way of segmentation. However, SAM's performance may decline when applied to tasks involving domains that differ from natural images. Nonetheless, by employing fine-tuning techniques, SAM exhibits promising capabilities in specific domains, such as medicine and planetary science. Notably, there is a lack of research on the application of SAM to sonar imaging. In this paper, we aim to address this gap by conducting a comprehensive investigation of SAM's performance on sonar images. Specifically, we evaluate SAM using various settings on sonar images. Additionally, we fine-tune SAM using effective methods both with prompts and for semantic segmentation, thereby expanding its applicability to tasks requiring automated segmentation. Experimental results demonstrate a significant improvement in the performance of the fine-tuned SAM.
