Relevant bibliographies by topics / Underwater Image Enhancement

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Underwater Image Enhancement'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 June 2025

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Journal articles on the topic "Underwater Image Enhancement"

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Wang, Qiu Yun. "Depth Estimation Based Underwater Image Enhancement." Advanced Materials Research 926-930 (May 2014): 1704–7. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.1704.

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According to the image formation model and the nature of underwater images, we find that the effect of the haze and the color distortion seriously pollute the underwater image data, lowing the quality of the underwater images in the visibility and the quality of the data. Hence, aiming to reduce the noise and the haze effect existing in the underwater image and compensate the color distortion, the dark channel prior model is used to enhance the underwater image. We compare the dark channel prior model based image enhancement method to the contrast stretching based method for image enhancement. The experimental results proved that the dark channel prior model has good ability for processing the underwater images. The super performance of the proposed method is demonstrated as well.
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Sethi, Rajni, and Sreedevi Indu. "Fusion of Underwater Image Enhancement and Restoration." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 03 (2019): 2054007. http://dx.doi.org/10.1142/s0218001420540075.

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Optical properties of water distort the quality of underwater images. Underwater images are characterized by poor contrast, color cast, noise and haze. These images need to be pre-processed so as to get some information. In this paper, a novel technique named Fusion of Underwater Image Enhancement and Restoration (FUIER) has been proposed which enhances as well as restores underwater images with a target to act on all major issues in underwater images, i.e. color cast removal, contrast enhancement and dehazing. It generates two versions of the single input image and these two versions are fused using Laplacian pyramid-based fusion to get the enhanced image. The proposed method works efficiently for all types of underwater images captured in different conditions (turbidity, depth, salinity, etc.). Results obtained using the proposed method are better than those for state-of-the-art methods.
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Shen, L., and Y. Zhao. "UNDERWATER IMAGE ENHANCEMENT BASED ON POLARIZATION IMAGING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2020 (August 6, 2020): 579–85. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2020-579-2020.

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Abstract. The need of high-quality underwater imaging is obviously required in many underwater applications. For example, underwater archaeology, underwater ecological research, underwater object detection and tracking. This paper presents a joint enhancing and denoising scheme for an image taken in underwater conditions. Conventional image enhancing methods may amplify the noise depending on the distance and density of the particles in the water. To suppress the noise and improve the enhancement performance, an imaging model is modified by adding the process of amplifying the noise in underwater conditions. This model offers depth-chromaticity compensation regularization for the transmission map and chromaticity-depth compensation regularization for enhancing the image. The proposed iterative underwater image enhancing method with polarization uses these two joint regularization schemes and the relationship between the transmission map and enhanced irradiance image. The transmission map and irradiance image are used to promote each other. To verify the effectiveness of the algorithm, polarizing images of different scenes in different conditions are collected. Different algorithms are applied to the original images. Experimental results demonstrate that the proposed scheme increases visibility in extreme conditions without amplifying the noise.
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Hu, Kai, Yanwen Zhang, Chenghang Weng, Pengsheng Wang, Zhiliang Deng, and Yunping Liu. "An Underwater Image Enhancement Algorithm Based on Generative Adversarial Network and Natural Image Quality Evaluation Index." Journal of Marine Science and Engineering 9, no. 7 (2021): 691. http://dx.doi.org/10.3390/jmse9070691.

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When underwater vehicles work, underwater images are often absorbed by light and scattered and diffused by floating objects, which leads to the degradation of underwater images. The generative adversarial network (GAN) is widely used in underwater image enhancement tasks because it can complete image-style conversions with high efficiency and high quality. Although the GAN converts low-quality underwater images into high-quality underwater images (truth images), the dataset of truth images also affects high-quality underwater images. However, an underwater truth image lacks underwater image enhancement, which leads to a poor effect of the generated image. Thus, this paper proposes to add the natural image quality evaluation (NIQE) index to the GAN to provide generated images with higher contrast and make them more in line with the perception of the human eye, and at the same time, grant generated images a better effect than the truth images set by the existing dataset. In this paper, several groups of experiments are compared, and through the subjective evaluation and objective evaluation indicators, it is verified that the enhanced image of this algorithm is better than the truth image set by the existing dataset.
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Brindha, J., and V. Vijayakumar. "Underwater Image Enhancement Using Histogram Method." Indonesian Journal of Electrical Engineering and Computer Science 8, no. 3 (2017): 687. http://dx.doi.org/10.11591/ijeecs.v8.i3.pp687-689.

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<p>The underwater images not only offer aninteresting sight, but also have a challenge to monitor marinespecies and underwater activities. Taking a beautiful underwater image requires extraordinary equipment and technique. Usually,there are distorted colors on the image caused by poor light andwater quality. So it requires an image enhancement process to geta proper photo to display. This research offers an improvedmethod of auto levels to produce stunning photos. This methoduses the color balancing based on the distribution of each channelR, G and B based on its histogram. The balancing of colors willreproduce colors more attractive compared with other methodsof auto level.<strong></strong></p>
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Lee, Ho Sang, Sang Whan Moon, and Il Kyu Eom. "Underwater Image Enhancement Using Successive Color Correction and Superpixel Dark Channel Prior." Symmetry 12, no. 8 (2020): 1220. http://dx.doi.org/10.3390/sym12081220.

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Underwater images generally suffer from quality degradations, such as low contrast, color cast, blurring, and hazy effect due to light absorption and scattering in the water medium. In applying these images to various vision tasks, single image-based underwater image enhancement has been challenging. Thus, numerous efforts have been made in the field of underwater image restoration. In this paper, we propose a successive color correction method with a minimal reddish artifact and a superpixel-based restoration using a color-balanced underwater image. The proposed successive color correction method comprises an effective underwater white balance based on the standard deviation ratio, followed by a new image normalization. The corrected image based on this color balance algorithm barely produces a reddish artifact. The superpixel-based dark channel prior is exploited to enhance the color-corrected underwater image. We introduce an image-adaptive weight factor using the mean of backscatter lights to estimate the transmission map. We perform intensive experiments for various underwater images and compare the performance of the proposed method with those of 10 state-of-the-art underwater image-enhancement methods. The simulation results show that the proposed enhancement scheme outperforms the existing approaches in terms of both subjective and objective quality.
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Qiao, Xiaorui, Yonghoon Ji, Atsushi Yamashita, and Hajime Asama. "Visibility Enhancement for Underwater Robots Based on an Improved Underwater Light Model." Journal of Robotics and Mechatronics 30, no. 5 (2018): 781–90. http://dx.doi.org/10.20965/jrm.2018.p0781.

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We propose an underwater image enhancement algorithm for improving underwater robot visibility. Images captured in underwater environments are typically degraded by the effects of absorption, scattering, and noise. Degraded images impede underwater robot task performance (e.g., inspection, detection, and visual simultaneous localization and mapping). In this study, we improve the underwater light model by considering floating particle noise and non-uniform illumination from artificial light sources. Specifically, a systematic underwater enhancement method that includes a floating particle removal algorithm and an image-dehazing algorithm is proposed. Our method is effective for underwater image enhancement applications in real-world scenarios. We compare and evaluate our proposed method with state-of-the-art methods, with an underwater evaluation and a feature-matching performance. The experimental results show that our method yields comparable (and even better) results than state-of-the-art methods.
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Luan, Xin, Guojia Hou, Zhengyuan Sun, Yongfang Wang, Dalei Song, and Shuxin Wang. "Underwater Color Image Enhancement Using Combining Schemes." Marine Technology Society Journal 48, no. 3 (2014): 57–62. http://dx.doi.org/10.4031/mtsj.48.3.8.

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AbstractUnderwater color image processing has received considerable attention in the last few decades for underwater image-based observation. In this article, a novel underwater image enhancement approach using combining schemes is presented. This study aims to improve color correction under nonuniform illumination conditions. The objective of this approach is threefold. First, to correct nonuniform illumination and enhance contrast in the image, homomorphic filtering is used. Second, the color contrast of an image is equalized by a contrast stretching algorithm in RGB (red, green and blue) color space. Finally, the noise amplified after the previous two steps is suppressed by using wavelet domain denoising based on threshold processing. The comparison of experimental results shows that the proposed approach of underwater image enhancement can correct the color imbalance and is especially suitable for processing underwater color images that have nonuniform lighting.
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Hu, Kai, Yanwen Zhang, Feiyu Lu, Zhiliang Deng, and Yunping Liu. "An Underwater Image Enhancement Algorithm Based on MSR Parameter Optimization." Journal of Marine Science and Engineering 8, no. 10 (2020): 741. http://dx.doi.org/10.3390/jmse8100741.

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The quality of underwater images is often affected by the absorption of light and the scattering and diffusion of floating objects. Therefore, underwater image enhancement algorithms have been widely studied. In this area, algorithms based on Multi-Scale Retinex (MSR) represent an important research direction. Although the visual quality of underwater images can be improved to some extent, the enhancement effect is not good due to the fact that the parameters of these algorithms cannot adapt to different underwater environments. To solve this problem, based on classical MSR, we propose an underwater image enhancement optimization (MSR-PO) algorithm which uses the non-reference image quality assessment (NR-IQA) index as the optimization index. First of all, in a large number of experiments, we choose the Natural Image Quality Evaluator (NIQE) as the NR-IQA index and determine the appropriate parameters in MSR as the optimization object. Then, we use the Gravitational Search Algorithm (GSA) to optimize the underwater image enhancement algorithm based on MSR and the NIQE index. The experimental results show that this algorithm has an excellent adaptive ability to environmental changes.
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AbuNaser, Amal, Iyad Abu Doush, Nahed Mansour, and Sawsan Alshattnawi. "Underwater Image Enhancement Using Particle Swarm Optimization." Journal of Intelligent Systems 24, no. 1 (2015): 99–115. http://dx.doi.org/10.1515/jisys-2014-0012.

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AbstractThis article introduces a framework for enhancing underwater images using the particle swarm optimization algorithm. A pre-processing step is introduced to reduce the absorbing and scattering effects of water before applying a filter based on this algorithm to enhance the image. The quality of enhanced images is quantitatively assessed by applying the framework on a dataset of underwater images. The obtained results show a considerable improvement.
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Dissertations / Theses on the topic "Underwater Image Enhancement"

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Partridge, William J. "Real time image enhancement during underwater recovery operations." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/26200.

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Mortazavi, Halleh. "Mitigation of contrast loss in underwater images." Thesis, University of Manchester, 2010. https://www.research.manchester.ac.uk/portal/en/theses/mitigation-of-contrast-loss-in-underwater-images(c469036e-66b1-48c9-9c8d-671061629ad8).html.

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The quality of an underwater image is degraded due to the effects of light scattering in water, which are resolution loss and contrast loss. Contrast loss is the main degradation problem in underwater images which is caused by the effect of optical back-scatter. A method is proposed to improve the contrast of an underwater image by mitigating the effect of optical back-scatter after image acquisition. The proposed method is based on the inverse model of an underwater image model, which is validated experimentally in this work. It suggests that the recovered image can be obtained by subtracting the intensity value due to the effect of optical back-scatter from the degraded image pixel and then scaling the remaining by a factor due to the effect of optical extinction. Three filters are proposed to estimate for optical back-scatter in a degraded image. Among these three filters, the performance of BS-CostFunc filter is the best. The physical model of the optical extinction indicates that the optical extinction can be calculated by knowing the level of optical back-scatter. Results from simulations with synthetic images and experiments with real constrained images in monochrome indicate that the maximum optical back-scatter estimation error is less than 5%. The proposed algorithm can significantly improve the contrast of a monochrome underwater image. Results of colour simulations with synthetic colour images and experiments with real constrained colour images indicate that the proposed method is applicable to colour images with colour fidelity. However, for colour images in wide spectral bands, such as RGB, the colour of the improved images is similar to the colour of that of the reference images. Yet, the improved images are darker than the reference images in terms of intensity. The darkness of the improved images is because of the effect of noise on the level of estimation errors.
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Kirli, Mustafa Yavuz. "3d Reconstruction Of Underwater Scenes From Uncalibrated Video Sequences." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/3/12609901/index.pdf.

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The aim of this thesis is to reconstruct 3D representation of underwater scenes from uncalibrated video sequences. Underwater visualization is important for underwater Remotely Operated Vehicles and underwater is a complex structured environment because of inhomogeneous light absorption and light scattering by the environment. These factors make 3D reconstruction in underwater more challenging. The reconstruction consists of the following stages: Image enhancement, feature detection and matching, fundamental matrix estimation, auto-calibration, recovery of extrinsic parameters, rectification, stereo matching and triangulation. For image enhancement, a pre-processing filter is used to remove the effects of water and to enhance the images. Two feature extraction methods are examined: 1. Difference of Gaussian with SIFT feature descriptor, 2. Harris Corner Detector with grey level around the feature point. Matching is performed by finding similarities of SIFT features and by finding correlated grey levels respectively for each feature extraction method. The results show that SIFT performs better than Harris with grey level information. RANSAC method with normalized 8-point algorithm is used to estimate fundamental matrix and to reject outliers. Because of the difficulties of calibrating the cameras in underwater, auto-calibration process is examined. Rectification is also performed since it provides epipolar lines coincide with image scan lines which is helpful to stereo matching algorithms. The Graph-Cut stereo matching algorithm is used to compute corresponding pixel of each pixel in the stereo image pair. For the last stage triangulation is used to compute 3D points from the corresponding pixel pairs.
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Sac, Hakan. "Opti-acoustic Stereo Imaging." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614782/index.pdf.

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In this thesis, opti-acoustic stereo imaging, which is the deployment of two-dimensional (2D) high frequency imaging sonar with the electro-optical camera in calibrated stereo configuration, is studied. Optical cameras give detailed images in clear waters. However, in dark or turbid waters, information coming from electro-optical sensor is insufficient for accurate scene perception. Imaging sonars, also known as acoustic cameras, can provide enhanced target details under these scenarios. To illustrate these visibility conditions, a 2D high frequency imaging sonar simulator as well as an underwater optical image simulator is developed. A computationally efficient algorithm is also proposed for the post-processing of the returned sonar signals. Where optical visibility allows, integration of the sonar and optical images effectively provides binocular stereo vision capability and enables the recovery of three-dimensional (3D) structural information. This requires solving the feature correspondence problem for these completely different sensing modalities. Geometrical interpretation of this problem is examined on the simulated optical and sonar images. Matching the features manually, 3D reconstruction performance of opti-acoustic system is also investigated. In addition, motion estimation from opti-acoustic image sequences is studied. Finally, a method is proposed to improve the degraded optical images with the help of sonar images. First, a nonlinear mapping is found to match local the features in opti-acoustical images. Next, features in the sonar image is mapped to the optical image using the transformation. Performance of the mapping is evaluated for different scene geometries.
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Rodrigues, Daily Daleno de O. "Realce Automático de Imagens Subaquáticas em Rios da Amazônia." Universidade Federal do Amazonas, 2015. http://tede.ufam.edu.br/handle/tede/4072.

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Submitted by Kamila Costa (kamilavasconceloscosta@gmail.com) on 2015-06-11T20:01:02Z No. of bitstreams: 1 Dissertação-Daily D de O Rodrigues.pdf: 2391223 bytes, checksum: 06b57d0d17da9e4844b2d8482ac25cb0 (MD5)<br>Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-06-15T18:06:50Z (GMT) No. of bitstreams: 1 Dissertação-Daily D de O Rodrigues.pdf: 2391223 bytes, checksum: 06b57d0d17da9e4844b2d8482ac25cb0 (MD5)<br>Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-06-15T18:08:28Z (GMT) No. of bitstreams: 1 Dissertação-Daily D de O Rodrigues.pdf: 2391223 bytes, checksum: 06b57d0d17da9e4844b2d8482ac25cb0 (MD5)<br>Made available in DSpace on 2015-06-15T18:08:28Z (GMT). No. of bitstreams: 1 Dissertação-Daily D de O Rodrigues.pdf: 2391223 bytes, checksum: 06b57d0d17da9e4844b2d8482ac25cb0 (MD5) Previous issue date: 2015-02-27<br>FAPEAM - Fundação de Amparo à Pesquisa do Estado do Amazonas<br>The enhancement of underwater images in applications in the area of Amazonian rivers has been increasingly required and needs further study especially where the rivers have high turbidity and low light. There is increasingly demand for automatic enhancement methods to carry out monitoring of fauna and flora intensive rivers, as well as for the maintenance of pipelines and underwater cables. The enhancement methods specified, developed and validated for using in the rivers of the Amazonia are faced with the problem of imaging quality. The research related to underwater am environments of the Amazon has to dead with high turbidity of the water, caused mainly due to particles in suspension and interaction of light with the environment. The underwater images extraction with satisfiable visibility of the environments of Amazonian rivers has become extremely indispensable and relevant, given that there are natural treasures still unexplored into the depths of these rivers, as well as there is need to maintain the underwater part of the transportation system gas LPG (Liquefied Petroleum Gas) Coari-Manaus. Given this promising scenario, this study aims to improve these images by applying techniques of enhancement using nonlinear filters, which promote the minimization of the light interaction characteristics with the environment, loss of contrast and color in images extracted from turbid underwater environments. The method was experimentally validated with images acquired from simulations of underwater scenes and images acquired in outdoor underwater environments. The proposed method is compared to two other techniques of highlighting or enhancement of images. As in this study, these techniques also require a single image as input. The results return images with enhanced visual quality, considering a large set of experiments with simulation data and real outdoors scenes.<br>O realce de imagens subaquáticas em aplicações na região dos rios amazônicos é cada vez mais requisitado e carece de um estudo mais aprofundado especialmente nos casos em que os rios apresentam alto índice de turbidez e baixa luminosidade. Estes rios têm demandado cada vez mais métodos de realce automáticos que realizem o monitoramento de sua fauna e flora, bem como manutenção de dutos e cabos subaquáticos. Os métodos de realce especificados, desenvolvidos e validados para uso nos rios da região, se deparam com o problema da qualidade de captação de imagens. As pesquisas relacionadas aos ambientes subaquáticos da Amazônia são prejudicadas pelo alto nível de turbidez de suas águas, causadas principalmente devido às partículas em suspensão e à interação da luz com o meio. A extração de imagens subaquáticas de visibilidade adequada aos ambientes dos rios amazônicos em geral, tem se demonstrado imprescindível e relevante, haja vista que, existem tesouros naturais ainda inexplorados nas profundezas desses rios. Por outro lado, verifica-se a necessidade de manutenção da parte subaquática do sistema de transporte de gás GLP (Gás Liquefeito de Petróleo) Coari-Manaus. Diante deste cenário promissor, este trabalho objetiva a melhoria dessas imagens através da aplicação de técnicas de realce com uso de filtros não lineares, que promovam a minimização das características da interação da luz com o meio, perda de contraste e cores em imagens extraídas de ambientes subaquáticos turvos. O método proposto é comparado a duas outras técnicas de realce ou melhoria de imagens que, como neste trabalho, também requerem uma única imagem como entrada. Os resultados obtidos retornam imagens com melhor qualidade visual, considerando-se um grande conjunto de experimentos realizados com dados de simulação e cenas reais obtidas em ambientes externos.
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Mahiddine, Amine. "Recalage hétérogène pour la reconstruction 3D de scènes sous-marines." Thesis, Aix-Marseille, 2015. http://www.theses.fr/2015AIXM4027/document.

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Le relevé et la reconstruction 3D de scènes sous-marine deviennent chaque jour plus incontournable devant notre intérêt grandissant pour l’étude des fonds sous-marins. La majorité des travaux existants dans ce domaine sont fondés sur l’utilisation de capteurs acoustiques l’image n’étant souvent qu’illustrative.L’objectif de cette thèse consiste à développer des techniques permettant la fusion de données hétérogènes issues d’un système photogrammétrique et d’un système acoustique.Les travaux présentés dans ce mémoire sont organisés en trois parties. La première est consacrée au traitement des données 2D afin d’améliorer les couleurs des images sous-marines pour augmenter la répétabilité des descripteurs en chaque point 2D. Puis, nous proposons un système de visualisation de scène en 2D sous forme de mosaïque.Dans la deuxième partie, une méthode de reconstruction 3D à partir d’un ensemble non ordonné de plusieurs images a été proposée. Les données 3D ainsi calculées seront fusionnées avec les données provenant du système acoustique dans le but de reconstituer le site sous-marin.Dans la dernière partie de ce travail de thèse, nous proposons une méthode de recalage 3D originale qui se distingue par la nature du descripteur extrait en chaque point. Le descripteur que nous proposons est invariant aux transformations isométriques (rotation, transformation) et permet de s’affranchir du problème de la multi-résolution. Nous validons à l’aide d’une étude effectuée sur des données synthétiques et réelles où nous montrons les limites des méthodes de recalages existantes dans la littérature. Au final, nous proposons une application de notre méthode à la reconnaissance d’objets 3D<br>The survey and the 3D reconstruction of underwater become indispensable for our growing interest in the study of the seabed. Most of the existing works in this area are based on the use of acoustic sensors image.The objective of this thesis is to develop techniques for the fusion of heterogeneous data from a photogrammetric system and an acoustic system.The presented work is organized in three parts. The first is devoted to the processing of 2D data to improve the colors of the underwater images, in order to increase the repeatability of the feature descriptors. Then, we propose a system for creating mosaics, in order to visualize the scene.In the second part, a 3D reconstruction method from an unordered set of several images was proposed. The calculated 3D data will be merged with data from the acoustic system in order to reconstruct the underwater scene.In the last part of this thesis, we propose an original method of 3D registration in terms of the nature of the descriptor extracted at each point. The descriptor that we propose is invariant to isometric transformations (rotation, transformation) and addresses the problem of multi-resolution. We validate our approach with a study on synthetic and real data, where we show the limits of the existing methods of registration in the literature. Finally, we propose an application of our method to the recognition of 3D objects
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Chen, Ying-Ching, and 陳英璟. "Underwater image enhancement: Using WavelengthCompensation and Image Dehazing (WCID)." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/94271506864231404657.

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碩士<br>國立中山大學<br>資訊工程學系研究所<br>99<br>Light scattering and color shift are two major sources of distortion for underwater photography. Light scattering is caused by light incident on objects reflected and deflected multiple times by particles present in the water before reaching the camera. This in turn lowers the visibility and contrast of the image captured. Color shift corresponds to the varying degrees of attenuation encountered by light traveling in the water with different wavelengths, rendering ambient underwater environments dominated by bluish tone. This paper proposes a novel approach to enhance underwater images by a dehazing algorithm with wavelength compensation. Once the depth map, i.e., distances between the objects and the camera, is estimated by dark channel prior, the light intensities of foreground and background are compared to determine whether an artificial light source is employed during image capturing process. After compensating the effect of artifical light, the haze phenomenon from light scattering is removed by the dehazing algorithm. Next, estimation of the image scene depth according to the residual energy ratios of different wavelengths in the background is performed. Based on the amount of attenuation corresponding to each light wavelength, color shift compensation is conducted to restore color balance. A Super-Rsolution image can offer more details that must be important and necessary in low resolution underwater image. In this paper combine Gradient-Base Super Resolution and Iterative Back-Projection (IBP) to propose Cocktail Super Resolution algorithm, with the bilateral filter to remove the chessboard effect and ringing effect along image edges, and improve the image quality. The underwater videos with diversified resolution downloaded from the Youtube website are processed by employing WCID, histogram equalization, and a traditional dehazing algorithm, respectively. Test results demonstrate that videos with significantly enhanced visibility and superior color fidelity are obtained by the WCID proposed.
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Jyun-HanHuang and 黃俊翰. "A systematic underwater image enhancement algorithm based on random forest." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/5an3hk.

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Tsai, Yu-Tai, and 蔡雨泰. "Single Image Dehazing, Rain/Snow Removal and Underwater Enhancement Using Digital Image Processing." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/20896594117992561651.

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碩士<br>國立臺灣大學<br>電信工程學研究所<br>102<br>Poor visibility in bad environment is a major problem for many applications of computer vision such as surveillance, intelligent vehicles, and outdoor object recognition, etc…. The reason is that the substantial presence of atmospheric particles has significant size and distribution in the participating medium. Based on this, weather conditions can be characterized as steady and dynamic cases. Specifically, steady bad weather such as fog and haze caused by microscopic particles is usually spatially and temporally consistent. Oppositely, dynamic bad weather such as rain and snow in made up of large particles. Because spatially and temporally neighboring areas are affected by rain and snow differently, the analysis is more difficult. However, the poor visibility in underwater photography is caused by light scattering and color shift. Color shift corresponds to the varying degrees of attenuation encountered by light traveling in the water with different wavelengths, rendering ambient underwater environments dominated by bluish tone. Light scattering is caused by light incident on objects reflected and deflected multiple times by particles present in the water before reaching the camera. This in turn lowers the visibility and contrast of the image captured. Under these conditions, the human viewer would be annoyed. They also degrade the effectiveness of any computer vision algorithm based on small features and varying degrees of attenuation. Therefore, it is necessary to model the visual effects for the various cases and then remove them. In this thesis, we introduce three existing typical single image dehazing methods: contrast-based, independent component analysis, and dark channel prior-based. To improve the dehazing quality, we propose a robust and effective dehazing method. Unlike other existing methods, there is the satisfactory dehazing quality during daytime and nighttime by our methods. And then, four existing typical rain and snow removal methods in single image: guidance image based image decomposition analysis, adaptive nonlocal means filter, and frequency-based analysis are also introduced in the literature. In this follows, we design a simple but effective method divide the rain or snow removal scheme into two parts, the first part is detection of rain or snow and the second part is inpainting. Besides, three existing typical underwater enhanced methods: histogram-based equalization, wavelength-based compensation, and fusion based are also introduced in the literature. In this follows, we design a simple but effective underwater enhanced method, and its main idea is combining the color correction, contrast stretching, and histogram equalization. Unlike other existing methods, we’ll get a better result which takes less processing time and highly enhances visibility and superior color fidelity by our method. We believe that we’ll run real-time on hardware in optimized circumstances.
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Peng, Yu-Ting, and 彭煜庭. "Underwater Image Enhancement by Rayleigh Stretching in Time and Frequency Domain." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/7z48kd.

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碩士<br>國立臺灣海洋大學<br>資訊工程學系<br>107<br>Light scattering and color shift are the main reasons to cause the underwater images distortion when the visible light propagates in the underwater. Scattering effect is brought by the suspended particles in the water that reflect the light in the other direction, it lowers visibility and contrast for the underwater images. The phenomenon of color shift is the presentation of blue or green due to the attenuation of the energy of different wavelengths when the light travels in the underwater. This thesis proposes a Rayleigh stretching-based method in the time and frequency domain to restore the underwater images. The method includes a few steps such as background color correction, Rayleigh stretching in time domain, Discrete Wavelet Transform (DWT) with Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the features, contrast adjustment, and sharpening the edges. Through the enhancement switching in the time and frequency domain, it can lower the impacts of noise and keep more information of underwater images. Experimental results through both the qualitative and quantitative analyses with the simulated underwater images and natural underwater images demonstrate that our approach greatly improves visibility and contrast, and obtains best values in terms of entropy, underwater image quality measure (UIQM) and feature similarity (FSIM), respectively.
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Books on the topic "Underwater Image Enhancement"

1

Partridge, William J. Image enhancement software for underwater recovery operations - user's manual. Naval Postgraduate School, 1989.

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Sue, Drafahl, ed. Digital imaging for the underwater photographer: Computer applications for photo enhancement and presentation. Amherst Media, Inc., 2002.

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Drafahl, Jack, and Sue Drafahl. Digital Imaging for the Underwater Photographer: Computer Applications for Photo Enhancement and Presentation. Amherst Media, Incorporated, 2012.

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Drafahl, Jack, and Sue Drafahl. Digital Imaging for the Underwater Photographer: Computer Applications for Photo Enhancement and Presentation. Amherst Media, 2001.

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Book chapters on the topic "Underwater Image Enhancement"

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Zhang, Can, Xu Zhang, and Dawei Tu. "Underwater Image Enhancement by Fusion." In Lecture Notes in Electrical Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5768-7_8.

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Sarma, Kaushik, and P. Vigneshwaran. "Underwater Image Enhancement Using Deep Learning." In Lecture Notes in Networks and Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84760-9_38.

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Manohar Prabhu, M., Shaikh Wajid Ali, Sumukh Suresh, and Lavanya Krishna. "Underwater Image Enhancement Algorithm Using Advanced Fusion." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1249-7_58.

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Chong, Chern How, Ahmad Shahrizan Abdul Ghani, and Kamil Zakwan Mohd Azmi. "Dual Image Fusion Technique for Underwater Image Contrast Enhancement." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5281-6_5.

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Lung Yao, Danny Ngo, and Abdullah Bade. "Underwater Enhanced Detail and Dehaze Technique (UEDD) for Underwater Image Enhancement." In Encyclopedia of Computer Graphics and Games. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-08234-9_375-1.

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Chiang, John Y., Ying-Ching Chen, and Yung-Fu Chen. "Underwater Image Enhancement: Using Wavelength Compensation and Image Dehazing (WCID)." In Advanced Concepts for Intelligent Vision Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23687-7_34.

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Ye, Xinchen, Hongcan Xu, Xiang Ji, and Rui Xu. "Underwater Image Enhancement Using Stacked Generative Adversarial Networks." In Advances in Multimedia Information Processing – PCM 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00764-5_47.

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Zhou, Yan, Qingwu Li, and Guanying Huo. "Underwater Moving Target Detection Based on Image Enhancement." In Advances in Neural Networks - ISNN 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59081-3_50.

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Sun, Xiaofan, Hao Liu, Xinsheng Zhang, and Kailian Deng. "Tricolor Pre-equalization Deblurring for Underwater Image Enhancement." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71589-6_52.

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Perez, Javier, Aleks C. Attanasio, Nataliya Nechyporenko, and Pedro J. Sanz. "A Deep Learning Approach for Underwater Image Enhancement." In Biomedical Applications Based on Natural and Artificial Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59773-7_19.

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Conference papers on the topic "Underwater Image Enhancement"

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Kim, Juhwan, Seokyong Song, and Son-Cheol Yu. "Denoising auto-encoder based image enhancement for high resolution sonar image." In 2017 IEEE Underwater Technology (UT). IEEE, 2017. http://dx.doi.org/10.1109/ut.2017.7890316.

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Mohamed, Nadir Mustafa A., Liqun Lin, Weiling Chen, and Hongan Wei. "Underwater Image Quality: Enhancement and Evaluation." In 2020 Cross Strait Radio Science & Wireless Technology Conference (CSRSWTC). IEEE, 2020. http://dx.doi.org/10.1109/csrswtc50769.2020.9372502.

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Zhu, Hang, and Rui Song. "Underwater Image Enhancement for Automotive Wading." In 2021 13th International Conference on Computer and Automation Engineering (ICCAE). IEEE, 2021. http://dx.doi.org/10.1109/iccae51876.2021.9426111.

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R, Vijay Anandh, Rukmani Devi S, Preetham S, Pratheep K, Punuru Bhanu Prakash Reddy, and Ram Aravind U. "QUALITATIVE ANALYSIS OF UNDERWATER IMAGE ENHANCEMENT." In 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2021. http://dx.doi.org/10.1109/icoei51242.2021.9453082.

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bt Shamsuddin, Norsila, Wan Fatimah bt Wan Ahmad, Baharum b. Baharudin, Mohd Kushairi, Mohd Rajuddin, and Farahwahida bt Mohd. "Significance level of image enhancement techniques for underwater images." In 2012 International Conference on Computer & Information Science (ICCIS). IEEE, 2012. http://dx.doi.org/10.1109/iccisci.2012.6297295.

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Cai, Chengyi, Yiheng Zhang, and Ting Liu. "Underwater Image Processing System for Image Enhancement and Restoration." In 2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN). IEEE, 2019. http://dx.doi.org/10.1109/iccsn.2019.8905310.

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Mi, Zetian, Zheng Liang, Yafei Wang, Xianping Fu, and Zhengyu Chen. "Multi-Scale Gradient Domain Underwater Image Enhancement." In 2018 OCEANS - MTS/IEEE Kobe Techno-Ocean (OTO). IEEE, 2018. http://dx.doi.org/10.1109/oceanskobe.2018.8559180.

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Liu, Hui, and Lap-Pui Chau. "Underwater image restoration based on contrast enhancement." In 2016 IEEE International Conference on Digital Signal Processing (DSP). IEEE, 2016. http://dx.doi.org/10.1109/icdsp.2016.7868625.

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Xiong, Jiaying, Yuxiang Dai, and Peixian Zhuang. "Underwater Image Enhancement by Gaussian Curvature Filter." In 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP). IEEE, 2019. http://dx.doi.org/10.1109/siprocess.2019.8868720.

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Li, Yujie, Huimin Lu, Jianru Li, Xin Li, and Seiichi Serikawa. "Underwater image enhancement using inherent optical properties." In 2015 IEEE International Conference on Information and Automation (ICIA). IEEE, 2015. http://dx.doi.org/10.1109/icinfa.2015.7279324.

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Reports on the topic "Underwater Image Enhancement"

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Giddings, Thomas, Cetin Savkli, and Joseph Shirron. Image Enhancement and Automated Target Recognition Techniques for Underwater Electro-Optic Imagery. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada521885.

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Giddings, Thomas, Cetin Savkli, and Joseph Shirron. Image Enhancement and Automated Target Recognition Techniques for Underwater Electro-Optic Imagery. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada546856.

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