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Artykuły w czasopismach na temat „Underwater image analysis”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Li, Chen Ming, Zhe Chen, Jie Shen, Xin Wang, and Hui Bin Wang. "Principal Component Analysis Based Underwater Object Recognition." Advanced Materials Research 850-851 (December 2013): 817–20. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.817.

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In this paper, the principal component analysis method is applied in the underwater image data for detecting the image objects. The system is designed to assist the underwater monitor system survey operations, specialized to the task of object identification. Firstly, the nature of the underwater is analyzed according to the image formation model and the appearance. Then, the discipline of the principal component analysis is theoretically analysis. Third, the principal component analysis method is applied in the underwater image for dimension reduction, extracting the image feather for recogni

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Priya, Jha, and Gupta Keerti. "Analysis Of Underwater Image De Hazing Approaches A Perspective View." International Journal of Trend in Scientific Research and Development 2, no. 6 (2018): 561–64. https://doi.org/10.31142/ijtsrd18588.

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Image de hazing is one of the pre processing steps in various computer vision applications. It is the process of improving the quality of image without any information loss. Usually the images are affected by various facts. Especially in underwater imagery, the haze and hue variations are greatly affected. This paper discusses the issues in underwater images and compares the existing image enhancement techniques for underwater images. Priya Jha | Keerti Gupta "Analysis Of Underwater Image De-Hazing Approaches: A Perspective View" Published in International Journal of Trend in Scienti

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Yang, Miao, Ge Yin, Haiwen Wang, Jinnai Dong, Zhuoran Xie, and Bing Zheng. "A Underwater Sequence Image Dataset for Sharpness and Color Analysis." Sensors 22, no. 9 (2022): 3550. http://dx.doi.org/10.3390/s22093550.

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The complex underwater environment usually leads to the problem of quality degradation in underwater images, and the distortion of sharpness and color are the main factors to the quality of underwater images. The paper discloses an underwater sequence image dataset called TankImage-I with gradually changing sharpness and color distortion collected in a pool. TankImage-I contains two plane targets, a total of 78 images. It includes two lighting conditions and three different water transparency. The imaging distance is also changed during the photographing process. The paper introduces the relev

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D, Prabhakar, Pujitha B, Naresh K, Nagarjuna K, and Dharani B. "Enhancing Underwater Images: A Comparative Analysis of Image Processing Techniques Using UCIQE." International Journal for Modern Trends in Science and Technology, no. 03 (March 25, 2025): 102–8. https://doi.org/10.5281/zenodo.15084874.

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As the technology of underwater shooting advances, the underwater image process has become necessary. For this research, five different methods have been applied to the underwater images, including white balance, Contrast enhancement, CLAHE, Global and local contrast enhancement, linear fusion and Gaussian Pyramid fusion method. The results of these different methods are compared using the method of Underwater Color Image Quality Evaluation (UCIQE). UCIQE can compute a score for each of the different outputs. The images with higher score imply better outcome of underwater image processing. The

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Yu, Yang, and Chenfeng Qin. "An End-to-End Underwater-Image-Enhancement Framework Based on Fractional Integral Retinex and Unsupervised Autoencoder." Fractal and Fractional 7, no. 1 (2023): 70. http://dx.doi.org/10.3390/fractalfract7010070.

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As an essential low-level computer vision task for remotely operated underwater robots and unmanned underwater vehicles to detect and understand the underwater environment, underwater image enhancement is facing challenges of light scattering, absorption, and distortion. Instead of using a specific underwater imaging model to mitigate the degradation of underwater images, we propose an end-to-end underwater-image-enhancement framework that combines fractional integral-based Retinex and an encoder–decoder network. The proposed variant of Retinex aims to alleviate haze and color distortion in th

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Wu, Zhe, Jianfgui Han, and Chenghao Cao. "Research on underwater image enhancement algorithm based on improved DCP." Journal of Physics: Conference Series 2083, no. 4 (2021): 042008. http://dx.doi.org/10.1088/1742-6596/2083/4/042008.

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Abstract All for underwater images, there are some drawbacks, such as low definition, serious color bias, dark brightness, etc. On the basis of in-depth analysis of common image enhancement algorithms, This paper uses the improved dark channel priority algorithm to enhance the underwater image, Improving the contrast of underwater images and color correction of underwater images. Color correction is added based on dark channel prior algorithm; Make the image look more even, higher contrast, more acceptable. The improved algorithm model has a higher transfer rate; PSNR is more balanced and has

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Saxena, Khushboo, and Yogesh Kumar Gupta. "Analysis of Image Processing Strategies Dedicated to Underwater Scenarios." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 3s (2023): 253–58. http://dx.doi.org/10.17762/ijritcc.v11i3s.6232.

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Underwater images undergo quality degradation issues of an image, like blur image, poor contrast, non-uniform illumination etc. Therefore, to process these degraded images, image processing come into existence. In this paper, two important image processing methods namely Image restoration and Image enhancement are compared. This paper also discusses the quality measures parameters of image processing which will be helpful to see clear images.

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Wang, Yi, Zhihua Chen, Guoxu Yan, Jiarui Zhang, and Bo Hu. "Underwater Image Enhancement Based on Luminance Reconstruction by Multi-Resolution Fusion of RGB Channels." Sensors 24, no. 17 (2024): 5776. http://dx.doi.org/10.3390/s24175776.

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Underwater image enhancement technology is crucial for the human exploration and exploitation of marine resources. The visibility of underwater images is affected by visible light attenuation. This paper proposes an image reconstruction method based on the decomposition–fusion of multi-channel luminance data to enhance the visibility of underwater images. The proposed method is a single-image approach to cope with the condition that underwater paired images are difficult to obtain. The original image is first divided into its three RGB channels. To reduce artifacts and inconsistencies in the f

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Pradnya Ravindra Narvekar, Manasi R. Dixit. "NNUIE-GAN: Near Natural Underwater Image Enhancement Based on Generative Adversarial Network." Journal of Information Systems Engineering and Management 10, no. 28s (2025): 281–89. https://doi.org/10.52783/jisem.v10i28s.4330.

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Underwater images are often prone to many non-linear distortions due to different underwater light interaction phenomenon. This contributes to colour distortion and low contrast which severely affects visual perception of that scene. Now, in today's world many underwater expeditions rely on visual perception of underwater world, which makes underwater image enhancement techniques very important. In the present work, Generative Adversarial Network based model NNUIE-GAN is introduced for real time underwater image enhancement. In this work, generator is a U Net based architecture which is tuned

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Jiao, Qingliang, Ming Liu, Pengyu Li, et al. "Underwater Image Restoration via Non-Convex Non-Smooth Variation and Thermal Exchange Optimization." Journal of Marine Science and Engineering 9, no. 6 (2021): 570. http://dx.doi.org/10.3390/jmse9060570.

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The quality of underwater images is an important problem for resource detection. However, the light scattering and plankton in water can impact the quality of underwater images. In this paper, a novel underwater image restoration based on non-convex, non-smooth variation and thermal exchange optimization is proposed. Firstly, the underwater dark channel prior is used to estimate the rough transmission map. Secondly, the rough transmission map is refined by the proposed adaptive non-convex non-smooth variation. Then, Thermal Exchange Optimization is applied to compensate for the red channel of

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Mohammed, Hesham Hashim, Shatha A. Baker, and Omar Ibrahim Alsaif. "An Improved Underwater Image Enhancement Approach for Border Security." Journal of Image and Graphics 12, no. 2 (2024): 199–204. http://dx.doi.org/10.18178/joig.12.2.199-204.

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Protecting maritime borders is crucial to ensuring overall border security. Law enforcement agencies make great use of analyzing images of underwater debris to gather intelligence and detect illicit materials. Underwater image improvement contributes to better data quality and analytical. Nevertheless, underwater image analysis poses greater challenges compared to analyzing images taken above the water, factors like refraction of light and darkness contribute to the degradation of underwater image quality. In this paper, a novel approach is proposed to enhance underwater images, the proposed a

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Liu, Jiapeng, Yi Liu, and Qiuping Jiang. "Delving into Underwater Image Utility: Benchmark Dataset and Prediction Model." Remote Sensing 17, no. 11 (2025): 1906. https://doi.org/10.3390/rs17111906.

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High-quality underwater images are essential for both human visual perception and machine analysis in marine vision applications. Although significant progress has been achieved in Underwater Image Quality Assessment (UIQA), almost all existing UIQA methods focus on the visual perception-oriented image quality issue and cannot be used to gauge the utility of underwater images for the use in machine vision applications. To address this issue, in this work, we focus on the problem of automatic underwater image utility assessment (UIUA). On the one hand, we first construct a large-scale Object De

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Jung, Young-Hwa, Gyuho Kim, and Woo Sik Yoo. "Off-Site Distortion and Color Compensation of Underwater Archaeological Images Photographed in the Very Turbid Yellow Sea." Journal of Conservation Science 38, no. 1 (2022): 14–32. http://dx.doi.org/10.12654/jcs.2022.38.1.02.

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Underwater photographing and image recording are essential for pre-excavation survey and during excavation in underwater archaeology. Unlike photographing on land, all underwater images suffer various quality degradations such as shape distortions, color shift, blur, low contrast, high noise levels and so on. Outcome is very often heavily photographing equipment and photographer dependent. Excavation schedule, weather conditions, and water conditions can put burdens on divers. Usable images are very limited compared to the efforts. In underwater archaeological study in very turbid water such a

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Zaheer, Sumbul. "A Triadic Approach for Enhancement of Underwater Images Using Adaptive Colour Correction with Unsharp Masking and CLAHE Implementation." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 5580–88. http://dx.doi.org/10.22214/ijraset.2024.62740.

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Abstract: The underwater domain has distinct challenges for capturing and examining images both. This is due to absorption and dispersion of light, which diminishes visual clarity and also distorts colour. In this context, we present an extensive method for enhancing underwater images with the objective of restoring true colours, uplifting contrast, and emphasizing minute details. Adaptive colour correction, detail sharpening, and contrast enhancement techniques drafted for underwater environments are all included in our project. Using objective picture quality standards includes the Underwate

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Teng, Lin, Yulong Qiao, and Shoulin Yin. "Underwater image denoising based on curved wave filtering and two-dimensional variational mode decomposition." Computer Science and Information Systems, no. 00 (2024): 57. http://dx.doi.org/10.2298/csis240314057t.

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Underwater image denoising technology is of great significance in underwater operation. Underwater operations (such as offshore oil drilling, undersea tunnels, pipeline construction, underwater archaeology, biological research, and lifesaving) require stable and clear underwater images to aid analysis. Due to the scattering and absorption of light by water bodies, obtaining high-quality under water images is a challenging task. Underwater images are prone to low contrast, low resolution and edge distortion. Therefore, it is difficult to accurately separate the effective signal when removing th

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Samarth, Borkar, and V. Bonde Sanjiv. "A Fusion Based Visibility Enhancement of Single Underwater Hazy Image." International Journal of Advances in Applied Sciences (IJAAS) 7, no. 1 (2018): 38–45. https://doi.org/10.11591/ijaas.v7.i1.pp38-45.

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Underwater images are prone to contrast loss, limited visibility, and undesirable color cast. For underwater computer vision and pattern recognition algorithms, these images need to be pre-processed. We have addressed a novel solution to this problem by proposing fully automated underwater image dehazing using multimodal DWT fusion. Inputs for the combinational image fusion scheme are derived from Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT) for contrast enhancement in HSV color space and color constancy using Shades of Gray algorithm respectively. To appraise the wo

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G., Nirmalapriya. "Comparative Analysis of Underwater and under Exposed Image Enhancement Techniques." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 192–200. http://dx.doi.org/10.5373/jardcs/v12sp7/20202098.

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Jha, Priya, and Keerti Gupta. "Analysis Of Underwater Image De-Hazing Approaches: A Perspective View." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 561–64. http://dx.doi.org/10.31142/ijtsrd18588.

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Borkar, Samarth, and Sanjiv V. Bonde. "A Fusion Based Visibility Enhancement of Single Underwater Hazy Image." International Journal of Advances in Applied Sciences 7, no. 1 (2018): 38. http://dx.doi.org/10.11591/ijaas.v7.i1.pp38-45.

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<span lang="EN-IN">Underwater images are prone to contrast loss, limited visibility, and undesirable color cast. For underwater computer vision and pattern recognition algorithms, these images need to be pre-processed. We have addressed a novel solution to this problem by proposing fully automated underwater image dehazing using multimodal DWT fusion. Inputs for the combinational image fusion scheme are derived from Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT) for contrast enhancement in HSV color space and color constancy using Shades of Gray algorithm respect

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Setiawan, Arif, Hadiyanto Hadiyanto, and Catur Edi Widodo. "Dimensional Reduction of Underwater Shrimp Digital Image Using the Principal Component Analysis Algorithm." E3S Web of Conferences 448 (2023): 02061. http://dx.doi.org/10.1051/e3sconf/202344802061.

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Shrimps are aquaculture products highly needed by the people and this is the reason their growth needs to be monitored using underwater digital images. However, the large dimensions of the shrimp digital images usually make the processing difficult. Therefore, this research focuses on reducing the dimensions of underwater shrimp digital images without reducing their information through the application of the Principal Component Analysis (PCA) algorithm. This was achieved using 4 digital shrimp images extracted from video data with the number of columns 398 for each image. The results showed th

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Cheng, Ruoshi, Caixia Zhang, Qingyang Xu, et al. "Underwater Fish Body Length Estimation Based on Binocular Image Processing." Information 11, no. 10 (2020): 476. http://dx.doi.org/10.3390/info11100476.

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Recently, the information analysis technology of underwater has developed rapidly, which is beneficial to underwater resource exploration, underwater aquaculture, etc. Dangerous and laborious manual work is replaced by deep learning-based computer vision technology, which has gradually become the mainstream. The binocular cameras based visual analysis method can not only collect seabed images but also construct the 3D scene information. The parallax of the binocular image was used to calculate the depth information of the underwater object. A binocular camera based refined analysis method for

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Porto Marques, Tunai, Alexandra Branzan Albu, and Maia Hoeberechts. "A Contrast-Guided Approach for the Enhancement of Low-Lighting Underwater Images." Journal of Imaging 5, no. 10 (2019): 79. http://dx.doi.org/10.3390/jimaging5100079.

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Underwater images are often acquired in sub-optimal lighting conditions, in particular at profound depths where the absence of natural light demands the use of artificial lighting. Low-lighting images impose a challenge for both manual and automated analysis, since regions of interest can have low visibility. A new framework capable of significantly enhancing these images is proposed in this article. The framework is based on a novel dehazing mechanism that considers local contrast information in the input images, and offers a solution to three common disadvantages of current single image deha

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Sari, Dewi Mutiara, Bayu Sandi Marta, Muhammad Amin A, and Haryo Dwito Armono. "The Analysis of Underwater Imagery System for Armor Unit Monitoring Application." International Journal of Artificial Intelligence & Robotics (IJAIR) 5, no. 1 (2023): 1–12. http://dx.doi.org/10.25139/ijair.v5i1.5918.

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The placement of armor units for breakwaters in Indonesia is still done manually, which depends on divers in each placement of the armor unit. The use of divers is less effective due to limited communication between divers and excavator operators, making divers in the water take a long time. This makes the diver's job risky and expensive. This research presents a vision system to reduce the diver's role in adjusting the position of each armor unit. This vision system is built with two cameras connected to a mini-computer. This system has an image improvement process by comparing three methods.

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Li, Jingsheng, Yuanbing Ouyang, Hao Wang, Di Wu, and Yushan Pan. "DeepSeaNet: An Efficient UIE Deep Network." Electronics 14, no. 12 (2025): 2411. https://doi.org/10.3390/electronics14122411.

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Underwater image enhancement and object recognition are crucial in multiple fields, like marine biology, archeology, and environmental monitoring, but face severe challenges due to low light, color distortion, and reduced contrast in underwater environments. DeepSeaNet re-evaluates the model guidance strategy from multiple dimensions, enhances color recovery using the MCOLE score, and addresses the problem of inconsistent attenuation across different regions of underwater images by integrating a feature extraction method guided by a global attention mechanism by ViT. Comprehensive tests on div

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Jiang, Pei. "APPLICATION OF 3D ANALYSIS TECHNOLOGY OF VISION SYSTEM IMAGE IN SPORTS MEDICINE." Revista Brasileira de Medicina do Esporte 27, no. 4 (2021): 381–85. http://dx.doi.org/10.1590/1517-8692202127042021_0123.

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ABSTRACT Background: Objective: The study of sports biomechanics in sports medicine usually requires a special image analysis system (software) to obtain 3D kinematics data. Taking the swimming project in sports medicine as an example, 3D water images in water have always been relatively complicated and difficult. As light travels in different media, it will refract and reflect. When testing underwater movements, if only a land camera or an underwater camera is used for testing, the error caused by light refraction will be larger, which will affect the accuracy of the test data even more. Meth

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Ayush Aditya, Om Prakash, Praveen, Yash Rathi, and Prof. Ramya K. "Implementation of Image Recognition for Human detection in Underwater Images." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 01 (2024): 1–5. http://dx.doi.org/10.47392/irjaeh.2024.0001.

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Recent advances in deep learning have resolved the challenges of detection of objects underwater. Specialized methods have been developed as a result of the particular characteristics of small, fuzzy objects and heterogeneous noise. The Sample-Weighted Network (SWIPE Net) for small object recognition is one of them, as are frameworks with feature enhancement and anchor refining. Additionally, upgraded versions of the attention processes and YOLOv7 have been released. These advancements help with tracking the effects of clean energy technologies, developing accurate and reliable underwater obje

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Skorohod, B. A., P. V. Zhiyakov, A. V. Statsenko, and S. I. Fateev. "Analysis of the accuracy of building 3D coordinates of the underwater robot workspace." Monitoring systems of environment, no. 3 (September 24, 2020): 163–70. http://dx.doi.org/10.33075/2220-5861-2020-3-163-170.

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Distortion of underwater images can impair both the accuracy and robustness of 3D scene reconstruction algorithms. The problems that arise are related to the lack of robustness of these methods to changes in the underwater environment and features of transmitting and receiving signals under water, including, in particular, uneven illumination of the underwater environment, rapid attenuation, scattering and refraction of light when passing through an inhomogeneous medium of air-water-glass, limiting the frequency spectrum of passing light, which leads to the absorption of low-frequency componen

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Oliveira, António José, Bruno Miguel Ferreira, and Nuno Alexandre Cruz. "A Performance Analysis of Feature Extraction Algorithms for Acoustic Image-Based Underwater Navigation." Journal of Marine Science and Engineering 9, no. 4 (2021): 361. http://dx.doi.org/10.3390/jmse9040361.

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In underwater navigation, sonars are useful sensing devices for operation in confined or structured environments, enabling the detection and identification of underwater environmental features through the acquisition of acoustic images. Nonetheless, in these environments, several problems affect their performance, such as background noise and multiple secondary echoes. In recent years, research has been conducted regarding the application of feature extraction algorithms to underwater acoustic images, with the purpose of achieving a robust solution for the detection and matching of environment

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Chen, Zhe, Jian Qiang Gao, Jie Shen, and Hui Bin Wang. "Spectral Residual Based Underwater Animal Detection." Advanced Materials Research 850-851 (December 2013): 970–73. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.970.

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In this paper, the spectral residual method is applied in the underwater image data for detecting the animals. The system is designed to assist the underwater monitor system survey operations, specialized to the task of animal detection. Firstly, the regularity for the frequency spectrum of the images collected in the underwater world is discovered by the statistical analysis. Then we transform the input image into the spatial frequency domain and singularities including in the frequency curve is extracted by average filtering. Finally, these singularities are inverse transformed from the freq

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Jia, Huidi, Yeqing Xiao, Qiang Wang, Xiai Chen, Zhi Han, and Yandong Tang. "Underwater Image Enhancement Network Based on Dual Layers Regression." Electronics 13, no. 1 (2024): 196. http://dx.doi.org/10.3390/electronics13010196.

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Due to the absorption and scattering of light in water, captured underwater images often suffer from some degradation, such as color cast, blur, and low contrast. These types of degradation usually affect and degrade the performance of computer vision methods and tasks under water. In order to solve these problems, in this paper, we propose a multi-stage and gradually optimized underwater image enhancement deep network, named DLRNet, based on dual layers regression. Our network emphasizes important information by aggregating different depth features in the channel attention module, and the dua

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Mehrunnisa, Mikolaj Leszczuk, Dawid Juszka, and Yi Zhang. "Improved Binary Classification of Underwater Images Using a Modified ResNet-18 Model." Electronics 14, no. 15 (2025): 2954. https://doi.org/10.3390/electronics14152954.

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In recent years, the classification of underwater images has become one of the most remarkable areas of research in computer vision due to its useful applications in marine sciences, aquatic robotics, and sea exploration. Underwater imaging is pivotal for the evaluation of marine eco-systems, analysis of biological habitats, and monitoring underwater infrastructure. Extracting useful information from underwater images is highly challenging due to factors such as light distortion, scattering, poor contrast, and complex foreground patterns. These difficulties make traditional image processing an

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Zhang, Shujing, Manyu Zhang, Yujie Cui, Xingyue Liu, Bo He, and Jiaxing Chen. "A fast ELM-based machine compression scheme for underwater image transmission on a low-bandwidth acoustic channel." Sensor Review 39, no. 4 (2019): 542–53. http://dx.doi.org/10.1108/sr-08-2018-0204.

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Purpose This paper aims to propose a fast machine compression scheme, which can solve the problem of low-bandwidth transmission for underwater images. Design/methodology/approach This fast machine compression scheme mainly consists of three stages. Firstly, raw images are fed into the image pre-processing module, which is specially designed for underwater color images. Secondly, a divide-and-conquer (D&C) image compression framework is developed to divide the problem of image compression into a manageable size. And extreme learning machine (ELM) is introduced to substitute for principal co

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Alenezi, Fayadh, Ammar Armghan, Sachi Nandan Mohanty, Rutvij H. Jhaveri, and Prayag Tiwari. "Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light." Water 13, no. 23 (2021): 3470. http://dx.doi.org/10.3390/w13233470.

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A lack of adequate consideration of underwater image enhancement gives room for more research into the field. The global background light has not been adequately addressed amid the presence of backscattering. This paper presents a technique based on pixel differences between global and local patches in scene depth estimation. The pixel variance is based on green and red, green and blue, and red and blue channels besides the absolute mean intensity functions. The global background light is extracted based on a moving average of the impact of suspended light and the brightest pixels within the i

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Vimal Raj, M., and S. Sakthivel Murugan. "Motion Deblurring Analysis for Underwater Image Restoration." Journal of Physics: Conference Series 1911, no. 1 (2021): 012028. http://dx.doi.org/10.1088/1742-6596/1911/1/012028.

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Wang, Keyan, Shirui Huang, and Yunsong Li. "An optical reconstruction based underwater image analysis." Journal of Image and Graphics 27, no. 5 (2022): 1337–58. http://dx.doi.org/10.11834/jig.210819.

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Jian, Muwei, Xiangyu Liu, Hanjiang Luo, Xiangwei Lu, Hui Yu, and Junyu Dong. "Underwater image processing and analysis: A review." Signal Processing: Image Communication 91 (February 2021): 116088. http://dx.doi.org/10.1016/j.image.2020.116088.

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Shubhangi N. Ghate. "Optimizing Underwater Vision: A Rigorous Investigation into CNN's Deep Image Enhancement for Subaquatic Scenes." Journal of Electrical Systems 20, no. 5s (2024): 2611–24. http://dx.doi.org/10.52783/jes.2703.

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In this paper, Convolutional Neural Networks were used to enhance the visual fidelity of underwater images. The UWCNN is introduced in this article, which utilizes underwater scene priors and a CNN model to improve underwater photos. The UWCNN model proposes a method where the clear latent underwater image is immediately rebuilt using the underwater scene as training data, rather than relying on parameter guessing in an underwater imaging model. Our UWCNN model may be used for frame-by-frame augmentation in underwater videos because to its lightweight network structure and efficient training d

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Shubhangi N. Ghate. "Optimizing Underwater Vision: A Rigorous Investigation into Cnn's Deep Image Enhancement for Subaquatic Scenes." Journal of Electrical Systems 20, no. 3 (2024): 593–605. http://dx.doi.org/10.52783/jes.2985.

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In this paper, Convolutional Neural Networks were used to enhance the visual fidelity of underwater images. The UWCNN is introduced in this article, which utilizes underwater scene priors and a CNN model to improve underwater photos. The UWCNN model proposes a method where the clear latent underwater image is immediately rebuilt using the underwater scene as training data, rather than relying on parameter guessing in an underwater imaging model. Our UWCNN model may be used for frame-by-frame augmentation in underwater videos because to its lightweight network structure and efficient training d

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S.Daniel Madan Raja. "Underwater Imagery Enhancement with Multi-Channel Histogram Equalization, Depth-Adaptive Correction, and Deep Reinforcement Learning." Journal of Electrical Systems 20, no. 5s (2024): 1984–92. http://dx.doi.org/10.52783/jes.2534.

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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 preserv

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Aessa, Suad Ali, Ekbal Hussain Ali, Salam Waley Shneen, and Layla H. Abood. "Adaptive Fuzzy Filter Technique for Mixed Noise Removing from Sonar Images Underwater." Journal of Fuzzy Systems and Control 2, no. 2 (2024): 45–49. https://doi.org/10.59247/jfsc.v2i2.176.

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Underwater Analysis of acquired images may be affected by low contrast, haze, and other disturbances., caused by scattering and absorption of the light through propagation. An adaptive fuzzy filter for three mixed noise reduction is adopted on underwater sonar images to take out the various noises that either appear in the image when captured or injected into the image when transmitted. Underwater images when captured usually have speckle noise, salt, pepper noise also Gaussian noise. Is suggested in this paper an adaptive fuzzy filter structure that combines the fuzzy filter, sigmoid sliding

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Asri, Sri Dianing. "Improving the Quality and Performance of Underwater Image Classification using the CLAHE-CNN Method." JSAI (Journal Scientific and Applied Informatics) 7, no. 2 (2024): 182–88. http://dx.doi.org/10.36085/jsai.v7i2.6417.

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Research on underwater image analysis is critical because of challenges such as color distortion, low contrast, and noise in images. Various methods have been proposed to overcome this problem. To improve the quality and classification of underwater photos, this study aims to ensure improved performance using Contrast Limited Adaptive Histogram Equalization (CLAHE) and Convolutional Neural Networks (CNN) on underwater image datasets. The dataset consists of 500 JPG image with RGB channel and dimensions of 512 × 512 collected from online sources. There are classifications of sharks, eels, dolph

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Zheng, Kun, Haoshan Liang, Hongwei Zhao, et al. "Application and Analysis of the MFF-YOLOv7 Model in Underwater Sonar Image Target Detection." Journal of Marine Science and Engineering 12, no. 12 (2024): 2326. https://doi.org/10.3390/jmse12122326.

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The need for precise identification of underwater sonar image targets is growing in areas such as marine resource exploitation, subsea construction, and ocean ecosystem surveillance. Nevertheless, conventional image recognition algorithms encounter several obstacles, including intricate underwater settings, poor-quality sonar image data, and limited sample quantities, which hinder accurate identification. This study seeks to improve underwater sonar image target recognition capabilities by employing deep learning techniques and developing the Multi-Gradient Feature Fusion YOLOv7 model (MFF-YOL

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Bane, Sumedh, Ritik Choudhary, Shashwat Gupta, and Kavita Tewari. "Comparative Analysis of Image Enhancement Algorithms." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 14, no. 02 (2022): 170–74. http://dx.doi.org/10.18090/samriddhi.v14i02.7.

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In the complex instruments utilised in essential fields such as satellite cameras, CT scanners, and High-Resolution Cameras (Underwater), image capture is critical without human-rated aberrations, sounds, or atmospheric disturbances. Even full reference QA (quality assessment) approaches have a limited ability to predict quality accurately. As a result, the difficulty of evaluating and enhancing photographs is further subdivided into domain-specific issues by focusing on a small set of artefacts. The most popular is entropy, which is usually relevant in picture coding: it is a lower limit for

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Chen, Jianfeng, Shidong Zhu, and Weilin Luo. "Instance Segmentation of Underwater Images by Using Deep Learning." Electronics 13, no. 2 (2024): 274. http://dx.doi.org/10.3390/electronics13020274.

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Based on deep learning, an underwater image instance segmentation method is proposed. Firstly, in view of the scarcity of underwater related data sets, the size of the data set is expanded by measures including image rotation and flipping, and image generation by a generative adversarial network (GAN). Next, the underwater image data set is finally constructed by manual labeling. Then, in order to solve the problems of color shift, blur and the poor contrast of optical images caused by the complex underwater environment and the attenuation and scattering of light, an underwater image enhanceme

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Li, Yonglong, Hua Zhang, Shuang Wang, Haoran Wang, and Jialong Li. "Image-Based Underwater Inspection System for Abrasion of Stilling Basin Slabs of Dam." Advances in Civil Engineering 2019 (October 1, 2019): 1–13. http://dx.doi.org/10.1155/2019/6924976.

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The abrasion of stilling basin slabs which is caused by waterborne particles is one of the main surface damages in the operation of hydropower station. For determining whether to repair the stilling basin slabs, periodic inspections of erosion condition of stilling basin slabs are required. The practical problem is how to get the underwater image without unwatering and how to analyse the abrasion though the images. This paper developed a novel underwater inspection system named UIS-1 which consists of a customized underwater robot and special quantitative analysis method for this situation. Fi

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Liu, Yidan, Huiping Xu, Dinghui Shang, Chen Li, and Xiangqian Quan. "An Underwater Image Enhancement Method for Different Illumination Conditions Based on Color Tone Correction and Fusion-Based Descattering." Sensors 19, no. 24 (2019): 5567. http://dx.doi.org/10.3390/s19245567.

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In the shallow-water environment, underwater images often present problems like color deviation and low contrast due to light absorption and scattering in the water body, but for deep-sea images, additional problems like uneven brightness and regional color shift can also exist, due to the use of chromatic and inhomogeneous artificial lighting devices. Since the latter situation is rarely studied in the field of underwater image enhancement, we propose a new model to include it in the analysis of underwater image degradation. Based on the theoretical study of the new model, a comprehensive met

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Robin, Hojiwala. "Image Color Balance with Laplacian and Gaussian Pyramid (CBLGP) Algorithm." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29427.

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In recent times, technology has rapidly and significantly evolved across all sectors. Digital image processing stands out as a modern technology aimed at achieving clear images. However, digitized images often encounter issues of low quality, such as unclear or underwater images that require enhancement for better visibility. These problems stem from factors like deficient focusing, lighting, and various constraints leading to low contrast, shading, and artifacts. Underwater and satellite images consistently face less-than-ideal conditions due to environmental factors like light refraction in

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Setiawan, Arif, Hadiyanto Hadiyanto, and Catur E. Widodo. "Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach." Revue d'Intelligence Artificielle 36, no. 6 (2022): 905–12. http://dx.doi.org/10.18280/ria.360611.

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Shrimp is a marine culture found globally due to the ability of its yields to boost a country's economy. It is imperative to monitor its size to determine the condition of the shrimp underwater with complex noise using a non-invasive method. Therefore, this study aims to develop a new method for measuring the body weight of shrimp using morphometric features based on underwater image analysis and a machine learning approach. The method used consists of several steps, data collection using an underwater camera, image analysis using image grayscale, image binary, edge detection, region of intere

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Li, Dejun, Tao Zhang, and Canjun Yang. "Terminal Underwater Docking of an Autonomous Underwater Vehicle Using One Camera and One Light." Marine Technology Society Journal 50, no. 6 (2016): 58–68. http://dx.doi.org/10.4031/mtsj.50.6.6.

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AbstractThis study introduces a vision guidance system for the terminal underwater docking of an autonomous underwater vehicle (AUV) using one camera and one light. The configuration of this docking system, including an overview of the AUV and the docking station, is proposed. A detailed description of the vision guidance system is then provided. Four stages, namely, image acquisition, binarization of the captured images, elimination of noisy luminaries, and estimation of the relative position, constitute the image processing procedure. A tracking control algorithm based on the position of the

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Liu, Dongcai, Xianhui Wen, and Youling Zhou. "Research on an improved fish recognition algorithm based on YOLOX." ITM Web of Conferences 47 (2022): 02003. http://dx.doi.org/10.1051/itmconf/20224702003.

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The key to the development of underwater resources is to detect underwater targets quickly and accurately in real time. However, due to the influence of light, the underwater image is easy to be distorted and the contrast is low and so on, which greatly affects the performance of the detection algorithm, In order to improve the detection accuracy of underwater targets, After a detailed analysis of the underwater detection target features, The attention mechanism ECA module was added to the YOLOX model, Real-ESRGAN was used to treat multiple target and fuzzy images in detection images, the accu

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