A Survey on Underwater Images Enhancement Techniques

Soni, Om Kumari; Kumare, Jamvant Singh

doi:10.1109/csnt48778.2020.9115732

Cited by 19 publications

(6 citation statements)

References 15 publications

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“…In the past, many surveys were written considering some particular or different aspect of underwater image analysis. Some of the past surveys are reported in (Schettini and Corchs (2010); Yang et al (2019); Wang et al (2019); Li et al (2020b); Soni and Kumare (2020); Fayaz et al (2020); Khurana and Tirpude (2020); Anwar and Li (2020); Jian et al (2021); Raveendran et al (2021); Mittal et al (2022); Xin Yuan (2022); Er (2022)). These surveys cover some particular aspects of underwater image analysis and rarely cover underwater surveillance modules.…”

Section: Analysis Of Existing Review and Survey Articlesmentioning

confidence: 99%

“…These surveys cover some particular aspects of underwater image analysis and rarely cover underwater surveillance modules. The articles (Yang et al (2019); Li et al (2020b); Soni and Kumare (2020); Khurana and Tirpude (2020); Anwar and Li (2020); Raveendran et al (2021)) discusses mainly, the underwater image enhancement techniques. Whereas, the articles Fayaz et al (2020), cover mostly underwater image restoration techniques.…”

Section: Analysis Of Existing Review and Survey Articlesmentioning

confidence: 99%

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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: Analysis Of Existing Review and Survey Articlesmentioning

confidence: 99%

Section: Analysis Of Existing Review and Survey Articlesmentioning

confidence: 99%

Passive Visual Underwater Surveillance: A Survey

Subudhi

Kapoor

Rout

et al. 2023

Preprint

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show abstract

“…However, the limited image contrast, especially in vertically uneven illumination conditions due to intense sunlight scattering in the upper visual fields and randomly polarized sunlight noise reflected from glazed surfaces of surroundings (6,7), substantially hinders high-quality imaging and consequently leads to low object detectability (fig. S1A) (8). Whereas an automobile driver can physically block the intense upper sunlight and mitigate polarized noise with a sun visor and sunglasses (fig.…”

Section: Introductionmentioning

confidence: 99%

Cuttlefish eye–inspired artificial vision for high-quality imaging under uneven illumination conditions

et al. 2023

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With the rise of mobile robotics, including self-driving automobiles and drones, developing artificial vision for high-contrast and high-acuity imaging in vertically uneven illumination conditions has become an important goal. In such situations, balancing uneven illumination, improving image contrast for facile object detection, and achieving high visual acuity in the main visual fields are key requirements. Meanwhile, in nature, cuttlefish (genus Sepia ) have evolved an eye optimized for vertically uneven illumination conditions, which consists of a W-shaped pupil, a single spherical lens, and a curved retina with a high-density photoreceptor arrangement and polarized light sensitivity. Here, inspired by the cuttlefish eye, we report an artificial vision system consisting of a W-shaped pupil, a single ball lens, a surface-integrated flexible polarizer, and a cylindrical silicon photodiode array with a locally densified pixel arrangement. The W-shaped pupil integrated on the ball lens balances vertically uneven illumination, and the cylindrical silicon photodiode array integrated with the flexible polarizer enables high-contrast and high-acuity imaging.

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“…To this end, various kinds of methods have been proposed to obtain information about the underwater environment to promote the use of marine resources. Among others, a crucial way to obtain information from the underwater environment is image understanding, while the images captured in realistic underwater scenarios usually have severe defects, such as blurriness, noise, and color distortion (Soni and Kumare, 2020).…”

Section: Introductionmentioning

confidence: 99%

Simultaneous restoration and super-resolution GAN for underwater image enhancement

et al. 2023

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Underwater images are generally of low quality, limiting the performance of subsequent perceptual tasks, such as underwater object detection and recognition. However, only a few methods can improve the quality of underwater images by simultaneously restoring and super-resolving underwater images. In this paper, we propose an end-to-end trainable model based on generative adversarial networks (GANs) called Simultaneous Restoration and Super-Resolution GAN (SRSRGAN) to obtain clear super-resolution underwater images automatically. In particular, our model leverages a cascaded architecture with two stages of carefully designed generative adversarial networks to restore and super-resolve corrupted underwater images in a coarse-to-fine manner. The major advantages of SRSRGAN are twofold. First, it is a unified solution that can simultaneously restore and super-resolve images. Second, SRSRGAN is not limited by the prior experience of the types and levels of underwater degraded images but can perform the inference using only observed corrupted images. These two advantages enable SRSRGAN to enjoy better flexibility and higher practicability in realistic underwater scenarios. Extensive experimental results demonstrate the superiority of SRSRGAN in underwater image restoration, super-resolution, and simultaneous restoration and super-resolution.

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A Survey on Underwater Images Enhancement Techniques

Cited by 19 publications

References 15 publications

Passive Visual Underwater Surveillance: A Survey

Passive Visual Underwater Surveillance: A Survey

Cuttlefish eye–inspired artificial vision for high-quality imaging under uneven illumination conditions

Simultaneous restoration and super-resolution GAN for underwater image enhancement

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