Speckle noise suppression of range gated underwater imaging system

Li, Hailan; Wang, Xia; Bai, Tingzhu; Jin, Weiqi; Huang, Youwei; Ding, Kun

doi:10.1117/12.831994

Cited by 15 publications

(9 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Supplementary Information-based Methods. In the earlier stage, supplementary information from multiple images [19] or specialized hardware devices (e.g., polarization filtering [20]- [23], range-gated imaging [24], [25], and fluorescence imaging [26], [27]) were utilized to improve the visibility of underwater images. Compared to supplementary informationbased methods, single underwater image enhancement has been proven to be more suitable for challenging situations such as dynamic scenes, and thus, gains extensive attention.…”

Section: A Underwater Image Enhancement Methodsmentioning

confidence: 99%

An Underwater Image Enhancement Benchmark Dataset and Beyond

Guo

Ren

et al. 2020

IEEE Trans. on Image Process.

1,245

672

View full text Add to dashboard Cite

Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world images. It is thus unclear how these algorithms would perform on images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater image enhancement using large-scale real-world images. In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 realworld underwater images, 890 of which have the corresponding reference images. We treat the rest 60 underwater images which cannot obtain satisfactory reference images as challenging data. Using this dataset, we conduct a comprehensive study of the stateof-the-art underwater image enhancement algorithms qualitatively and quantitatively. In addition, we propose an underwater image enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs). The benchmark evaluations and the proposed Water-Net demonstrate the performance and limitations of state-of-the-art algorithms, which shed light on future research in underwater image enhancement. The dataset and code are available at https://li-chongyi.github.io/proj benchmark.html.

show abstract

Section: A Underwater Image Enhancement Methodsmentioning

confidence: 99%

An Underwater Image Enhancement Benchmark Dataset and Beyond

Guo

Ren

et al. 2020

IEEE Trans. on Image Process.

1,245

672

View full text Add to dashboard Cite

show abstract

“…Supplementary information-based methods: In these methods, the supplementary information from multiple images is used to improve the quality of underwater images including polarization filtering [24] and range-gated imaging [8]. It is necessary to take multiple images to get sufficient information, which makes it more complicated than a single image method.…”

Section: Underwater Image Enhancement Methodsmentioning

confidence: 99%

Real-time Image Enhancement for Vision-based Autonomous Underwater Vehicle Navigation in Murky Waters

Chen

Rahmati

Sadhu

et al. 2019

Proceedings of the International Conference on Underwater Networks &Amp; Systems

View full text Add to dashboard Cite

Classic vision-based navigation solutions, which are utilized in algorithms such as Simultaneous Localization and Mapping (SLAM), usually fail to work underwater when the water is murky and the quality of the recorded images is low. That is because most SLAM algorithms are feature-based techniques and often it is impossible to extract the matched features from blurry underwater images. To get more useful features, image processing techniques can be used to dehaze the images before they are used in a navigation/localization algorithm. There are many well-developed methods for image restoration, but the degree of enhancement and the resource cost of the methods are different. In this paper, we propose a new visual SLAM, specifically-designed for the underwater environment, using Generative Adversarial Networks (GANs) to enhance the quality of underwater images with underwater image quality evaluation metrics. This procedure increases the efficiency of SLAM and gets a better navigation and localization accuracy. We evaluate the proposed GANs-SLAM combination by using different images with various levels of turbidity in the water. Experiments were conducted and the data was extracted from the Carnegie Lake in Princeton, and the Raritan river both in New Jersey, USA.

show abstract

“…1. Hardware-based approaches use additional devices for image acquisition, for instance, directly blocking the backscattered signal through range gated imaging (Li et al 2009;Tan et al 2005Tan et al , 2006, taking at least two static scene images with different orientations of a polarization filter in front of the camera (Schechner et al 2001(Schechner et al , 2003Karpel 2004, 2005;Schechner and Averbuch 2007;Shwartz and Schechner 2006) or the light source (Dubreuil et al 2013;Hu et al 2018;Huang et al 2016;Schechner 2006, 2008), capture images by a light field camera system (Skinner and Johnson-Roberson 2017) or a stereo imaging system (Roser et al 2014). 2.…”

Section: Backscatter Removalmentioning

confidence: 99%

Optical Imaging and Image Restoration Techniques for Deep Ocean Mapping: A Comprehensive Survey

Song

Nakath

She

et al. 2022

PFG

View full text Add to dashboard Cite

Visual systems are receiving increasing attention in underwater applications. While the photogrammetric and computer vision literature so far has largely targeted shallow water applications, recently also deep sea mapping research has come into focus. The majority of the seafloor, and of Earth’s surface, is located in the deep ocean below 200 m depth, and is still largely uncharted. Here, on top of general image quality degradation caused by water absorption and scattering, additional artificial illumination of the survey areas is mandatory that otherwise reside in permanent darkness as no sunlight reaches so deep. This creates unintended non-uniform lighting patterns in the images and non-isotropic scattering effects close to the camera. If not compensated properly, such effects dominate seafloor mosaics and can obscure the actual seafloor structures. Moreover, cameras must be protected from the high water pressure, e.g. by housings with thick glass ports, which can lead to refractive distortions in images. Additionally, no satellite navigation is available to support localization. All these issues render deep sea visual mapping a challenging task and most of the developed methods and strategies cannot be directly transferred to the seafloor in several kilometers depth. In this survey we provide a state of the art review of deep ocean mapping, starting from existing systems and challenges, discussing shallow and deep water models and corresponding solutions. Finally, we identify open issues for future lines of research.

show abstract

Speckle noise suppression of range gated underwater imaging system

Cited by 15 publications

References 0 publications

An Underwater Image Enhancement Benchmark Dataset and Beyond

An Underwater Image Enhancement Benchmark Dataset and Beyond

Real-time Image Enhancement for Vision-based Autonomous Underwater Vehicle Navigation in Murky Waters

Optical Imaging and Image Restoration Techniques for Deep Ocean Mapping: A Comprehensive Survey

Contact Info

Product

Resources

About