A Study on Underwater Image Processing Techniques

In this chapter, the receding horizon Kalman filter is applied to underwater navigation systems. The ocean covers about two-thirds of the earth and has a great effect on human beings. However, the ocean is overlooked while we focus our attention on land and atmospheric issues; we have not been able to explore the full depths of the ocean, its abundant livings and non-living resources. For example, only recently we have discovered, by using manned submersibles, that a large amount of methane and carbon dioxide comes from the seafloor and extraordinary groups of organisms live in hydrothermal vent areas. However, a number of complex issues due to the unstructured and hazardous undersea environment make it difficult to survey in the ocean even though today’s technologies have allowed humans to land on the moon and robots to travel to Mars. Unmanned underwater vehicles (UUVs) can help us better understand marine and other environmental issues, protect the ocean resources of the earth from pollution, and efficiently utilize them for human welfare. The UUV is a platform for a variety of sensors: acoustic, magnetic, gravimetric and chemical ones. Most commercial UUVs are tethered and remotely operated, referred to as remotely operated vehicles (ROVs). Extensive use of manned submersibles and ROVs are currently limited to a few applications because of very high operational costs, operator fatigue and safety issues. The demand for advanced underwater vehicle technologies is growing and will eventually lead to fully autonomous and reliable underwater vehicles. Autonomous underwater vehicles (AUVs) were initially developed to perform missions that were not easy for ROVs and manned underwater vehicles. Since the autonomy allows AUVs to be used for risky missions such as a mine countermeasure (MCM) or under-ice operations, AUVs are replacing ROVs towed vehicles as well as manned underwater vehicles (Whitcomb, 2000). For detailed ocean surveys, an AUV acts as a more stable platform for precision sensors than ROVs or towed vehicles because an AUV is not subject to physical disturbances transmitted along the cable to the surface vessel. This absence of physical attachment also allows AUVs to measure ocean characteristics at specific depths and perform bottom-following missions as owing to its autonomy. In short, An AUV provides marine researchers with a new form of access to deeper ocean. For an AUV to successfully complete a typical survey mission, it must follow a path specified by the operator as closely as possible and arrive at a precise location for collecting data. When an AUV is not able to follow the path accurately during the mission, critical Source: Kalman Filter, Book edited by: Vedran Kordic, ISBN 978-953-307-094-0, pp. 390, May 2010, INTECH, Croatia, downloaded from SCIYO.COM

There have been increased developments in ocean exploration using autonomous underwater vehicles (AUVs) and unmanned underwater vehicles (UUVs). However, the contrast of underwater images is still a major issue for application. It is difficult to acquire clear underwater images around underwater vehicles. Since the 1960s, sonar sensors have been extensively used to detect and recognize objects in oceans. Due to the principles of acoustic imaging, sonar-imaged images have many shortcomings, such as a low signal to noise ratio and a low resolution. Consequently, vision sensors must be used for short-range identification because sonars yield to low-quality images. This thesis will concentrate solely on the optical imaging sensors for ocean observing. Although the underwater optical imaging technology makes a great progress, the recognition of underwater objects also remains a major issue in recent days. Different from the common images, underwater images suffer from poor visibility due to the medium scattering and light distortion. First of all, capturing images underwater are difficult, mostly due to attenuation caused by light. The random attenuation of the light mainly causes the haze appearance along with the part of the light scattered back from the water. In particular, the objects at a distance of more than 10 m are almost indistinguishable because of absorption. Furthermore, when the artificial light is employed, it can cause a distinctive footprint on the seafloor. In this paper, we will analysis the recent trends of ocean exploration approaches.

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 35821, “A Mobile Platform for AI-Powered Underwater Image Processing,” by Chenyu Zhou, University of Houston; Lily Walker, William & Mary; and Xin Fu, University of Houston, et al. The paper has not been peer reviewed. Copyright 2025 Offshore Technology Conference. _ Timely inspection of subsea infrastructure—particularly pipelines—is crucial for preventing oil spills and protecting the environment. This paper introduces a technology for offshore pipeline inspection centered on an autonomous robotic system equipped with underwater computer vision and edge-computing capabilities. The aim is to create a fast, cost-effective solution for inspecting underwater structures, enabling early detection of environmental hazards. Introduction Autonomous underwater vehicles (AUVs) have revolutionized the ability to explore and monitor marine environments. The cornerstone of AUV capabilities lies in their imaging systems, although the underwater environment poses unique and complex challenges for these systems. Additional image-processing and correction ability remains a goal for AUVs, creating computational demands that must be balanced against limited onboard power and processing capabilities (Fig. 1). Recently, advancements in deep learning have enabled deep neural networks (DNNs) to achieve remarkable success in solving a wide range of image-processing challenges, significantly surpassing the performance of traditional methods. In this research, the authors build upon foundational work in the literature to present a comprehensive workflow for adapting and deploying pretrained DNN models onto mobile computing platforms suitable for integration with AUVs. The described approach examines and documents the transition from powerful desktop systems to resource-constrained mobile platforms. Underwater Image Enhancement Motion blur is a common challenge in underwater image processing, especially when the camera is mounted on a moving platform such as an AUV. The lack of adequate lighting in underwater environments often requires longer exposure times, further exacerbating motion blur. The blurring process can be mathematically described as a convolution of the sharp image with a blur kernel, plus noise. If the blur kernel is known beforehand, the problem becomes one of nonblind deconvolution. However, when the blur kernel is unknown—common in dynamic underwater settings—it becomes a blind deconvolution problem. This problem is inherently ill-posed because multiple combinations of sharp images and blur kernels can explain the same blurred image. To resolve this ambiguity, assumptions or previous knowledge about the blur kernel or image structure typically are introduced. Traditional blind deconvolution methods often use iterative algorithms, refining the blur kernel and sharp image estimates over multiple steps. These classical approaches, however, often suffer from artifacts such as ringing and struggling with large motions or noise. To address these limitations, researchers have explored regularization techniques that incorporate priors to constrain the solution space. For example, sparsity priors in the temporal domain encourage smooth results, while Bayesian frameworks provide probabilistic formulations of the problem. One promising direction involves leveraging domain-specific priors for underwater imagery. By incorporating this property into optimization frameworks, researchers have developed more-effective and -robust blind deblurring methods tailored to underwater scenarios.

Region-specialized underwater image restoration in inhomogeneous optical environments

The paper explores the problems of restoring underwater images exposed to distortions in the form of color and contrast deformations, the presence of haze, etc., arising from the interaction of optical radiation with the aquatic environment. Restoring underwater images is a non-trivial task due to the large variability of the parameters of the aquatic environment and photography conditions. The proposed method, unlike other underwater image recovery algorithms based on an imaging model, is not based on a simplified exponential Beer-Lambert law for estimating optical radiation attenuation in water, but on a more accurate physical approach that simulates the propagation of optical rays in water using the Monte Carlo method, taking into account the main parameters the water environment and the camera. The results of numerical simulation of optical ray propagation in an aquatic environment are used for image processing in the spatial domain by editing the histograms of each image channel in the RGB color space. To test the developed algorithm, 6 real underwater images were selected obtained under various lighting conditions (natural and artificial) and various parameters of the aquatic environment (clear ocean and turbid coastal water). For the purpose of qualitative and quantitative analysis of the obtained results, the following similar underwater image processing methods were used: Fusion, UDCP IATP, Retinex, HE, and UWB VCSE. The Underwater Colour Image Quality Evaluation Metric (UCIQE) and Underwater Image Quality Measure (UIQM) indicators were used to quantify the results obtained. The results of the qualitative assessment demonstrate the high efficiency of the proposed method: regardless of the conditions of the initial image parameters, the application of the developed method improves visual perception and does not lead to excessive contrast enhancement, color distortion, loss of detail, the appearance of artifacts, etc. Quantification of underwater image processing results demonstrates comparable and superior results when comparing the efficiency of the algorithm with similar methods. For the UCIQE parameter, the developed method provided an improvement from 9 % to 51 % relative to the parameter value for the original image, while similar methods demonstrated results from minus 10 % to 82 %. For the UIQM parameter, the developed method provided an improvement from 24 % to 99 % relative to the parameter value for the original image, while similar methods demonstrated results from minus 10 % to 123 %. Unlike analogues, the developed method did not demonstrate the worst values of the UCIQE and UIQM parameters for any processed image, which indicates the stability of the method regardless of the parameters of the aquatic environment and shooting conditions. By dividing the developed method into preliminary and main stages, high image processing speed is ensured: 0.073 seconds for images with a resolution of 400 × 300 pixels and from 8.02 to 8.23 seconds for images with a resolution of 5184 × 3456 pixels. Similar methods demonstrated values from 0.19 to 10.81 seconds for an image with a resolution of 400 × 300 pixels and from 7.65 to 937.83 seconds for an image with a resolution of 5184 × 3456 pixels. The introduction of the proposed method into the geological exploration will increase their efficiency and reliability, and will provide more accurate data for further exploration of solid mineral deposits. Such technique integrated into the machine vision system of underwater vehicles will significantly expand their functionality by enabling automation of operations and improving the efficiency of recognition systems.

Underwater image processing should balance image clarity restoration and comprehensive display of underwater scenes, requiring image fusion and stitching techniques. The pixel level fusion method is based on pixels, and by fusing different image data, it eliminates stitching gaps and sudden changes in lighting intensity, preserves detailed information, and thus improves the accuracy of stitching images. In the process of restoring underwater video images without local priors, there is still room for optimization in steps such as removing atmospheric light values, estimating transmittance, and calculating dehazing images through regularization. Based on the characteristics of Jerlov water types, water quality is classified according to the properties of suspended solids, and each channel is adjusted to the compensation space to improve the restoration algorithm. Background light estimation is used to determine the degree of image degradation, select the optimal attenuation coefficient ratio, and restore the image. The experimental results show that it is crucial to choose a ratio of attenuation coefficients that is close to the actual water quality environment being photographed. Both this model and traditional algorithms have an accuracy rate of over 99.0%, with the accuracy of this model sometimes reaching 99.9%. Pixel level fusion and background light estimation technology optimize underwater images, improve stitching accuracy and clarity, enhance target detection and recognition, and have important value for marine exploration rigs.

Owing to absorption and scattering of light in water, raw underwater images suffer from low contrast, blurred details, and color casts. These characteristics significantly interfere the visibility of underwater images. In this paper, we propose a new robust adversarial learning framework via physics model based feedback control and domain adaptive mechanism for underwater image enhancement. A new method for simulating underwater-like training dataset by underwater image formation model is proposed. Upon this, a novel framework, which introduces a domain adaptive mechanism and a physics model constraint feedback control, is trained to enhance the underwater scenes. Extensive experiments demonstrate the significant superiority of the proposed framework compared with the state-of-the-art methods.

In various inspections for the construction and the maintenance of port facilities, the underwater observation is a very important and an effective method. In general, the view of underwater video images is narrowly limited due to the turbidity of the sea water and the camera field angle. Therefore, the data processing such as mosaicing is necessary to observe the object over the wide range. However, the automization of mosaicing work is difficult, because of the insufficiency of the position information about underwater images. We have proposed an efficient mosaicing system of underwater images using AUV (autonomous underwater vehicle) mounting the video camera with the line-laser. It is preferable to take a movie from the same direction by a constant distance as much as possible to make an accurate mosaicing image efficiently. In proposed system, the attitude of AUV is controlled based on the distance and the inclination to the object calculated from the position of the laser line in obtained images. To make mosaicing image by proposed method, the image of the port structure with the laser line is acquired in a real sea area. And, the affine transformation, the projective transformation, and the method of using information about the laser line are used as a transformation method of images extracted from a movie of an actual port structure. In this paper, applicability and the improvement method of mosaicing procedure are discussed by comparing mosaicing images obtained by these methods.

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper OTC 28312, “Understanding the Global Scientific Value of Industry ROV Data To Quantify Marine Ecology and Guide Offshore Decommissioning Strategies,” by D.L. McLean, University of Western Australia; P. Macreadie, Deakin University; D.J. White, University of Southampton and University of Western Australia; P.G. Thomson, University of Western Australia; A. Fowler, New South Wales Department of Primary Industries; A.R. Gates, National Oceanography Centre, UK; M. Benfield, Louisiana State University; T. Horton, National Oceanography Centre; D. Skropeta, University of Wollongong; T. Bond, University of Western Australia; D.J. Booth, University of Technology, Australia; E. Techera, C. Pattiaratchi, and S.P. Collin, University of Western Australia; D.O.B. Jones, National Oceanography Centre; L. Smith, Woodside Energy; and J.C. Partridge, University of Western Australia, prepared for the 2018 Offshore Technology Conference Asia, 20–23 March, Kuala Lumpur. The paper has not been peer reviewed. Copyright 2018 Offshore Technology Conference. Reproduced by permission. Remotely operated vehicles (ROVs) are used by the offshore oil and gas industry to monitor the integrity of subsea infrastructure and, in doing so, collect terabytes of video and in-situ physical data from inaccessible regions and poorly understood marine environments. The complete paper describes the potential global scientific value of video and other data collected•by ROVs. ROVs and the Offshore Oil and Gas Industry Nearly one-third of global production of oil and gas is sourced offshore. Tens of thousands of offshore wells have been drilled, and more than 900 large-scale offshore oil and gas platforms have been installed. The offshore industry relies on underwater observation, control, and intervention, tasks performed increasingly by ROVs; over 700 were in operation globally at the time the paper was written. The advancement of ROV technology for the industry was prompted in the 1970s by developments extending into water depths beyond the capabilities of divers, and by the desire to reduce reliance on diving operations. Modern ROVs fall into two main classes: work class and observation class. A work-class ROV is car-sized, usually cuboidal, with a buoyant upper part containing power equipment, thrusters, manipulators, and cameras. An umbilical connection to the surface vessel provides electrical and fiber-optic connections that allow control and data transfer. Observation-class ROVs are smaller—sometimes briefcase-sized—with limited power and manipulation capabilities, and are used primarily for inspection purposes. For observation missions that can be conducted without real-time intervention, autonomous underwater vehicles (AUVs) are increasingly popular. These are generally torpedo-shaped, with higher hydrodynamic stability than ROVs, and can offer a similar image-gathering platform. Oil and gas projects and other ocean-based industries such as offshore wind use ROVs at multiple stages in a project’s life. Surveys of the region before installation or construction are conducted using ROVs or AUVs to identify the bathymetry and substrate conditions for hazard assessments and to provide parameters for engineering design. Drilling activities commonly involve ROV monitoring at the seabed level, and, when the production facilities are installed, ROVs are used to perform observation of all subsea activities. During operation of a project, ROVs are used for inspection and intervention. The manipulators operate valves and interchange components, and, if required, can undertake cutting. Inspections include visual surveys as well as checking of anode electropotentials and clearance of marine fouling. Inspections commonly are undertaken annually but can be more or less frequent depending on risk level and maintenance requirements. In total, the ocean around a typical oil and gas development may be visited dozens of times by ROVs during the project’s life.

For the past few years, underwater exploration has increased exponentially. Currently available instruments for data collections (Side Scan Sonar, Multi Beam echo sounder, sub bottom profiler and Remotely Operated Vehicle) in underwater research and observation not only provide the data on objects and species, but also provide data about the sea surface. In this regard, selecting suitable features is a huge task. Due to limited datasets in Underwater, it is difficult to classify the objects/features from underwater images. In order to overcome this, machine learning based Bag of Features model is adopted in this paper. The dataset is obtained from shallow water using ROV. Since the underwater optical images have low light intensity, making the classification of features a difficult task; SURF (Speeded-Up Robust Features) and SVM (Support Vector Machines) algorithms are implemented in Bag of Features model to attain maximum accuracy. The performance evaluation of training and testing datasets gives better performances.

In this study, we propose a CNN-GAN-based real-time processing technique for filtering images of underwater cables used in power systems. This addresses the excessive interference impurities that are frequently observed in images captured by remotely operated vehicles (ROVs). The process begins with the input of the original image into the convolutional neural network (CNN). Subsequently, the training outcomes, which serve as input parameters for the generative adversarial network (GAN), facilitate the filtering process. The system also calculates both the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR), performing model updates via backward propagation. This technique utilizes deep learning technologies to achieve rapid, real-time filtering of underwater cable images. The experimental results reveal that the loss function of the CNN reaches 0.16 with an accuracy of 97.5%, while the loss function of the adversarial GAN network approaches 0.05. Compared with traditional methods such as DDN, JORDOR, RESCAN, and PRENet, the proposed CNN-GAN algorithm exhibits superior performance, as evidenced by the higher PSNR and SSIM values. Specifically, for clear water images, the PSNR reaches 29.86 dB and the SSIM is 0.9045. For severely polluted images, the PSNR is 28.67 dB and the SSIM is 0.8965, while for unevenly illuminated images, the PSNR and SSIM values are 24.37 dB and 0.88, respectively. These enhancements significantly benefit the monitoring and maintenance of power systems.

In the computer vision field, underwater object detection has been a challenging task. Due to the attenuation of light in a medium and the scattering of light by suspended particles in water, underwater optical images often face the problems of color distortion and target feature blurring, which greatly affect the detection accuracy of underwater object detection. Although deep learning-based algorithms have achieved state-of-the-art results in the field of object detection, most of them cannot be applied to practice because of the limited computing capacity of a low-power processor embedded in unmanned underwater vehicles. This paper proposes a lightweight underwater object detection network based on the YOLOX model called LUO-YOLOX. A novel weighted ghost-CSPDarknet and simplified PANet were used in LUO-YOLOX to reduce the parameters of the whole model. Moreover, aiming to solve the problems of color distortion and unclear features of targets in underwater images, this paper proposes an efficient self-supervised pre-training joint framework based on underwater auto-encoder transformation (UAET). After the end-to-end pre-training process with the self-supervised pre-training joint framework, the backbone of the object detection network can extract more essential and robust features from degradation images when retrained on underwater datasets. Numerous experiments on the URPC2021 and detecting underwater objects (DUO) datasets verify the performance of our proposed method. Our work can assist unmanned underwater vehicles to perform underwater object detection tasks more accurately.

The advantages of using unmanned underwater vehicles in coastal ocean studies are emphasized. Two types of representative vehicles, remotely operated vehicle (ROV) and autonomous underwater vehicle (AUV) from University of South Florida, are discussed. Two individual modular sensor packages designed and tested for these platforms and field measurement results are also presented. The bottom classification and albedo package, BCAP, provides fast and accurate estimates of bottom albedos, along with other parameters such as in-water remotes sensing reflectance. The real-time ocean bottom optical topographer, ROBOT, reveals high-resolution 3-dimentional bottom topography for target identification. Field data and results from recent Coastal Benthic Optical Properties field campaign, 1999 and 2000, are presented. Advantages and limitations of these vehicles and applications of modular sensor packages are compared and discussed.

Underwater images suffer from blurring effects, low contrast, and grayed out colors due to the absorption and scattering effects under the water. Many image enhancement algorithms for improving the visual quality of underwater images have been developed. Unfortunately, no well-accepted objective measure exists that can evaluate the quality of underwater images similar to human perception. Predominant underwater image processing algorithms use either a subjective evaluation, which is time consuming and biased, or a generic image quality measure, which fails to consider the properties of underwater images. To address this problem, a new nonreference underwater image quality measure (UIQM) is presented in this paper. The UIQM comprises three underwater image attribute measures: the underwater image colorfulness measure (UICM), the underwater image sharpness measure (UISM), and the underwater image contrast measure (UIConM). Each attribute is selected for evaluating one aspect of the underwater image degradation, and each presented attribute measure is inspired by the properties of human visual systems (HVSs). The experimental results demonstrate that the measures effectively evaluate the underwater image quality in accordance with the human perceptions. These measures are also used on the AirAsia 8501 wreckage images to show their importance in practical applications.

Conventional underwater pipeline inspections typically utilize Remotely Operated Vehicles (ROVs) tethered to Dynamic Positioning Class 2 (DP2) vessels, which entail significant expenses. To reduce these expenses, alternative inspection approaches have been explored. The Robotic Autonomous Underwater Vehicle (AUV) offers a cost-effective alternative by eliminating the need for a DP2 vessel and can instead be deployed using a more economical DP1 vessel, pioneering the underwater pipeline inspection. This game-changing Robotic AUV with Electric Field Gradient Sensor (EFGS) integration using a DP1 vessel can reduce inspection costs by up to 55% compared to using a ROV with a DP2 vessel. Additionally, it can reduce inspection durations from 19 days to just 7 days (a 60% reduction), significantly lowering the cost and carbon footprint due to the shorter execution time. The development of the AUV technology integrated with EFGS was a collaborative effort between PETRONAS and AUV developer, ROVULA. This innovative technology has successfully completed its pilot deployment in November 2024, inspecting a 30″ pipeline over a total length of 155 km offshore Sarawak. The Robotic AUV can achieve superior inspection coverage compared to conventional ROV methods and accomplishes the task in less time. This advantage is due to the untethered operation of the lightweight AUV, which allows it to move twice as fast as the ROV and eliminates the need for physical contact with the pipeline. The EFGS technology performs non-contact cathodic protection (CP) measurements. Unlike the conventional ROV CP stab method, which requires the ROV to frequently stop and stab the CP probe onto the anodes and bare metal (if exposed), the AUV with EFGS moves along the pipeline without stoppage. It accurately measures the field gradient along the entire pipeline without contacting the anodes or pipeline, even if it is buried.