Underwater image restoration based on diffraction bounded optimization algorithm with dark channel prior

Mathias, Ajisha; Dhanalakshmi, Samiappan

doi:10.1016/j.ijleo.2019.06.025

Cited by 28 publications

(5 citation statements)

References 9 publications

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“…Similarly, an underwater dark channel prior (UDCP) strategy is used by Hou et al (2020) and Liang et al (2021) for the estimation of transmission map. Schemes like Mathias and Samiappan (2019), Ueki and Ikehara (2019) have also used DCP for the estimation of transmission maps.…”

Section: Image Prior Based Techniquesmentioning

confidence: 99%

Passive Visual Underwater Surveillance: A Survey

Subudhi

Kapoor

Rout

et al. 2023

Preprint

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The objective of this article is to provide a detailed review of the state-of-the-art underwater surveillance process and the new trends of the same. Underwater surveillance has recently getting lots of attention because of its potential applications including the security of the coastal border, effective fish farming, deep-sea exploration, preservation of rare aquatic animals, etc. In the case of underwater, the light that is sensed by the camera got degraded due to many underwater phenomena such as haze, low illumination, scattering, absorption, diffraction, and refraction. The actual color of the object, as well as the scene, gets degraded as the light can not travel deep underwater. In a situation, where the scene of view is not clear, it is very difficult to detect the moving objects present in the scene. Further, the identification of the object of concern and tracking of that becomes more challenging. In this survey, we categorize underwater surveillance as a combination of the three blocks: enhancement, object detection, and object tracking. We categorize all these three blocks based on the underwater complexity and motion models considered. In this article, we tried to enumerate the most detailed descriptions of each category. We also discuss the future directions of research in the area of underwater surveillance.

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Section: Image Prior Based Techniquesmentioning

confidence: 99%

Passive Visual Underwater Surveillance: A Survey

Subudhi

Kapoor

Rout

et al. 2023

Preprint

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“…It assumes that in a haze-free image most of the local patches should contain at least one color channel with a very low intensity, but in a real image in fog, the more fog is in between the observer and the scene, the more the "dark" channels appear brighter. DCP inspired the development of single image dehazing methods and later this scene-depth derivation method has also been intensively applied in single underwater image enhancement (Chao and Wang 2010; Chiang and Chen 2011;Li et al 2016a, b;Mathias and Samiappan 2019;Yang et al 2011;Zhao et al 2015). Nevertheless, due to the severe attenuation of red light in underwater images, the standard DCP result does not fit for underwater scenarios and requires some modifications: (Carlevaris-Bianco et al 2010) computes the intensity difference between the red channel and the maximum of the green and blue channels per-patch which terms maximum intensity prior (MIP).…”

Section: Scene Distance Estimationmentioning

confidence: 99%

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

Song

Nakath

She

et al. 2022

PFG

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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.

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“…Underwater images need improvement for a variety of applications such as object detection, tracking, and other surveillances due to visibility degradations and geometric distortions. The Dark Channel Prior (DCP) approach [4,15] is the most commonly used method for restoring hazy or blurred images. The DCP method estimates the Background Light (BL) and Transmission Map (TM) for image restoration by calculating the depth map values of the red channel in the image.…”

Section: Data Pre-processing Using Proposed Diffraction Limited Image...mentioning

confidence: 99%

Deep Neural Network Driven Automated Underwater Object Detection

Mathias¹,

Dhanalakshmi²,

Kumar³

et al. 2022

Computers, Materials &Amp; Continua

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Object recognition and computer vision techniques for automated object identification are attracting marine biologist's interest as a quicker and easier tool for estimating the fish abundance in marine environments. However, the biggest problem posed by unrestricted aquatic imaging is low luminance, turbidity, background ambiguity, and context camouflage, which make traditional approaches rely on their efficiency due to inaccurate detection or elevated false-positive rates. To address these challenges, we suggest a systemic approach to merge visual features and Gaussian mixture models with You Only Look Once (YOLOv3) deep network, a coherent strategy for recognizing fish in challenging underwater images. As an image restoration phase, pre-processing based on diffraction correction is primarily applied to frames. The YOLOv3 based object recognition system is used to identify fish occurrences. The objects in the background that are camouflaged are often overlooked by the YOLOv3 model. A proposed Bi-dimensional Empirical Mode Decomposition (BEMD) algorithm, adapted by Gaussian mixture models, and integrating the results of YOLOv3 improves detection efficiency of the proposed automated underwater object detection method. The proposed approach was tested on four challenging video datasets, the Life Cross Language Evaluation Forum (CLEF) benchmark from the F4K data repository, the University of Western Australia (UWA) dataset, the bubble vision dataset and the DeepFish dataset. The accuracy for fish identification is 98.5 percent, 96.77 percent, 97.99 percent and 95.3 percent respectively for the various datasets which demonstrate the feasibility of our proposed automated underwater object detection method.

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Underwater image restoration based on diffraction bounded optimization algorithm with dark channel prior

Cited by 28 publications

References 9 publications

Passive Visual Underwater Surveillance: A Survey

Passive Visual Underwater Surveillance: A Survey

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

Deep Neural Network Driven Automated Underwater Object Detection

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