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Relevant bibliographies by topics / Underwater image analysis

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Academic literature on the topic 'Underwater image analysis'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Underwater image analysis"

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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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Dissertations / Theses on the topic "Underwater image analysis"

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Hansen, Joseph T. "Link budget analysis for undersea acoustic signaling." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02Jun%5FHansen.pdf.

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Kaeli, Jeffrey W. "Computational strategies for understanding underwater optical image datasets." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85539.

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Thesis: Ph. D. in Mechanical and Oceanographic Engineering, Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Department of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 117-135).<br>A fundamental problem in autonomous underwater robotics is the high latency between the capture of image data and the time at which operators are able to gain a visual understanding of the survey environment. Typical missions can generate im

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Garcia, Jorge F. "Assessing the performabce of omni-directional receivers for passing acoustic detection of vocalizing odontocetes : initial analysis /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02Dec%5FGarcia.pdf.

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Thesis (M.S. in Physical Oceanography)--Naval Postgraduate School, December 2002.<br>Thesis advisor(s): Ching-Sang Chiu, Curtis A. Collins. Includes bibliographical references (p. 45). Also available online.

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Åhlén, Julia. "Colour Correction of Underwater Images Using Spectral Data." Doctoral thesis, Uppsala University, Centre for Image Analysis, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-6138.

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<p>For marine sciences sometimes there is a need to perform underwater photography. Optical properties of light cause severe quality problems for underwater photography. Light of different energies is absorbed at highly different rates under water causing significant bluishness of the images. If the colour dependent attenuation under water can be properly estimated it should be possible to use computerised image processing to colour correct digital images using Beer’s Law.</p><p>In this thesis we have developed such estimation and correction methods that have become progressively more complica

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Kelsall, A. "Flexible Shape Models for Marine Animal Detection in Underwater Images." Thesis, Honours thesis, University of Tasmania, 2005. https://eprints.utas.edu.au/248/1/afkThesis_FINAL.pdf.

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Many industries are benefiting from computer automation, however the area of image analysis is still limited. The process of finding a potential object in an image is hard in itself, let alone classifying it. Automating these tasks would significantly reduce the time it takes to complete them thus allowing much more data to be processed. This becomes a problem when data is collect faster than it can be analysed. Images and video sequences are captured for different purposes and need to be manually processed in order to discover their contents. The fishing industry is a perfect example of th

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Clark, Tad Dee. "An Analysis of Microstructure and Corrosion Resistance in Underwater Friction Stir Welded 304L Stainless Steel." Diss., BYU ScholarsArchive, 2005. http://contentdm.lib.byu.edu/ETD/image/etd872.pdf.

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Andersson, Adam. "Range Gated Viewing with Underwater Camera." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-4244.

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<p>The purpose of this master thesis, performed at FOI, was to evaluate a range gated underwater camera, for the application identification of bottom objects. The master thesis was supported by FMV within the framework of “arbetsorder Systemstöd minjakt (Jan Andersson, KC Vapen)”. The central part has been field trials, which have been performed in both turbid and clear water. Conclusions about the performance of the camera system have been done, based on resolution and contrast measurements during the field trials. Laboratory testing has also been done to measure system specific parameters, s

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Norström, Christer. "Underwater 3-D imaging with laser triangulation." Thesis, Linköping University, Department of Electrical Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-6125.

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<p>The objective of this master thesis was to study the performance of an active triangulation system for 3-D imaging in underwater applications. Structured light from a 20 mW laser and a conventional video camera was used to collect data for generation of 3-D images. Different techniques to locate the laser line and transform it into spatial coordinates were developed and evaluated. A field- and a laboratory trial were performed.</p><p>From the trials we can conclude that the distance resolution is much higher than the lateral- and longitudinal resolution. The lateral resolution can be improv

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Törnblom, Nils. "Underwater 3D Surface Scanning using Structured Light." Thesis, Uppsala universitet, Centrum för bildanalys, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-138205.

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In this thesis project, an underwater 3D scanner based on structured light has been constructed and developed. Two other scanners, based on stereoscopy and a line-swept laser, were also tested. The target application is to examine objects inside the water filled reactor vessel of nuclear power plants. Structured light systems (SLS) use a projector to illuminate the surface of the scanned object, and a camera to capture the surfaces' reflection. By projecting a series of specific line-patterns, the pixel columns of the digital projector can be identified off the scanned surface. 3D points can t

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Hoth, Julian [Verfasser], and Wojciech [Akademischer Betreuer] Kowalczyk. "Development and Analysis of Physics-based Models for Autonomous Underwater Vehicle Navigation and the Reconstruction of Underwater Images / Julian Hoth. Betreuer: Wojciech Kowalczyk." Duisburg, 2016. http://d-nb.info/1102896942/34.

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Books on the topic "Underwater image analysis"

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Lauren, Fletcher, Klute Glenn K, and United States. National Aeronautics and Space Administration., eds. Evaluation of lens distortion errors using an underwater camera system for video-based motion analysis. National Aeronautics and Space Administration, 1994.

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Book chapters on the topic "Underwater image analysis"

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Kawahara, Ryo, Meng-Yu Jennifer Kuo, and Takahiro Okabe. "Polarimetric Underwater Stereo." In Image Analysis. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31438-4_35.

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Åhlén, J., D. Sundgren, T. Lindell, and E. Bengtsson. "Dissolved Organic Matters Impact on Colour Reconstruction in Underwater Images." In Image Analysis. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499145_116.

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Åhlén, Julia, and David Sundgren. "Bottom Reflectance Influence on a Color Correction Algorithm for Underwater Images." In Image Analysis. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45103-x_121.

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Antich, Javier, and Alberto Ortiz. "Underwater Cable Tracking by Visual Feedback." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_7.

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Greig, Alistair R. "Application of the Hough transform for weld inspection underwater." In Image Analysis and Processing. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60298-4_339.

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Ghosh, Bidisha, Michael O’Byrne, Franck Schoefs, and Vikram Pakrashi. "Inspection methods and image analysis." In Image-Based Damage Assessment for Underwater Inspections. CRC Press, 2018. http://dx.doi.org/10.1201/9781351052580-2.

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Pieroni, Goffredo G., Gian Luca Foresti, and Vittorio Murino. "Integration of optical and acoustical imaging sensors for underwater applications." In Image Analysis and Processing. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63508-4_192.

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Murino, Vittorio, Enrico Frumento, and Flavio Gabino. "Restoration of noisy underwater acoustic images using Markov Random Fields." In Image Analysis and Processing. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60298-4_281.

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Bozzano, Roberto, and Antonio Siccardi. "Underwater vegetation detection in high frequency sonar images: A preliminary approach." In Image Analysis and Processing. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63508-4_170.

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Boudiaf, Abderrahmene, Yuhang Guo, Adarsh Ghimire, et al. "Underwater Image Enhancement Using Pre-trained Transformer." In Image Analysis and Processing – ICIAP 2022. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06433-3_41.

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Conference papers on the topic "Underwater image analysis"

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tao, yucheng, Xianhua Duan, Kaixi Lu, and donghai Ni. "Enhancing YOLOv7 for underwater image object detection: a research study." In International Conference on Pattern Recognition and Image Analysis, edited by Mingguang Shan and Tao Lei. SPIE, 2025. https://doi.org/10.1117/12.3056159.

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Ghei, Anushka, Dhriti Rajesh Krishnan, Gautam Santhosh, and S. Natarajan. "Systematic Analysis of Underwater Image Dehazing and Object Detection." In 2024 6th Asia Symposium on Image Processing (ASIP). IEEE, 2024. http://dx.doi.org/10.1109/asip63198.2024.00011.

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Tang, Yijie, and Jin Wang. "Underwater SLAM system based on image feature point information enhancement." In Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), edited by Ji Zhao and Yonghui Yang. SPIE, 2024. http://dx.doi.org/10.1117/12.3038010.

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Rani, Sangeeta, Anand Singh Jalal, and Subhash Chand Agrawal. "Review and Analysis of Underwater Image Enhancement Based Techniques." In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0. IEEE, 2024. http://dx.doi.org/10.1109/otcon60325.2024.10687779.

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Gu, Fei, Wenxing Xu, Zhengyin Liang, Yong Wang, and Yi Yang. "YOLO-DS: research on lightweight algorithm for underwater small target detection by introducing dynamic upsampler." In International Conference on Pattern Recognition and Image Analysis, edited by Mingguang Shan and Tao Lei. SPIE, 2025. https://doi.org/10.1117/12.3056100.

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Seo, Dongmin, Sangwoo Oh, and Seungoh Han. "Design of Biomimetic Optical Sensor for Underwater Fluid Velocity Measurement." In 3D Image Acquisition and Display: Technology, Perception and Applications. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/3d.2024.jm4a.1.

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To develop an optical sensor that mimics the neuromast, a sensory organ in the fish used to detect movement, vibration and pressure gradient, we propose a design of a pillar, a sensor structure with robust properties in the underwater, and present conditions and analysis results for simulating the displacement of the pillar in the flow field.

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Kayalvizhi, S., and S. Kanthalakshmi. "Enhanced Underwater Image Dehazing Using Dark Channel Prior: A Comparative Analysis of Transmission Map Estimation Methods." In 2025 International Conference on Computational Innovations and Engineering Sustainability (ICCIES). IEEE, 2025. https://doi.org/10.1109/iccies63851.2025.11032783.

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Keerthana, N. V., V. Srija, R. Poornima Devi, B. Roopini, and J. M. Sharumathi. "Underwater Corrosive Material Identification and Image Enhancement." In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV). IEEE, 2025. https://doi.org/10.1109/icvadv63329.2025.10961150.

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Kao, Hao-Chun, Ching-Yi Yang, Yaw-Huei Lee, and Yi-Chih Chow. "Processing and Analysis of Phase-Locked Cavitation Images on a Marine Propeller Surface." In 2025 IEEE Underwater Technology (UT). IEEE, 2025. https://doi.org/10.1109/ut61067.2025.10947393.

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Abin, Deepa, Spandan Surdas, Atharva Suryawanshi, Khushi Thakare, and Yash Saravane. "A Hybrid Approach for Underwater Image Enhancement." In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714899.

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Reports on the topic "Underwater image analysis"

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Schoening, Timm. OceanCV. GEOMAR, 2022. http://dx.doi.org/10.3289/sw_5_2022.

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OceanCV provides computer vision algorithms and tools for underwater image analysis. This includes image processing, pattern recognition, machine learning and geometric algorithms but also functionality for navigation data processing, data provenance etc.

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Mbani, Benson, Valentin Buck, and Jens Greinert. Megabenthic Fauna Detection with Faster R-CNN (FaunD-Fast) Short description of the research software. GEOMAR, 2023. http://dx.doi.org/10.3289/sw_1_2023.

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This is an A.I. - based workflow for detecting megabenthic fauna from a sequence of underwater optical images. The workflow (semi) automatically generates weak annotations through the analysis of superpixels, and uses these (refined and semantically labeled) annotations to train a Faster R-CNN model. Currently, the workflow has been tested with images of the Clarion-Clipperton Zone in the Pacific Ocean

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King, E. L., A. Normandeau, T. Carson, et al. Pockmarks, a paleo fluid efflux event, glacial meltwater channels, sponge colonies, and trawling impacts in Emerald Basin, Scotian Shelf: autonomous underwater vehicle surveys, William Kennedy 2022011 cruise report. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331174.

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A short but productive cruise aboard RV William Kennedy tested various new field equipment near Halifax (port of departure and return) but also in areas that could also benefit science understanding. The GSC-A Gavia Autonomous Underwater Vehicle equipped with bathymetric, sidescan and sub-bottom profiler was successfully deployed for the first time on Scotian Shelf science targets. It surveyed three small areas: two across known benthic sponge, Vazella (Russian Hat) within a DFO-directed trawling closure area on the SE flank of Sambro Bank, bordering Emerald Basin, and one across known pockmar

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