GUDCP: Generalization of Underwater Dark Channel Prior for Underwater Image Restoration

Liang, Zheng; Ding, Xueyan; Yan, Xiaohong; Fu, Xianping

doi:10.1109/tcsvt.2021.3114230

Cited by 89 publications

(19 citation statements)

References 28 publications

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“…Zhou et al (2022) have proposed a scheme to compensate for the red channel by the perfect reflection assumption algorithm (PRAA) and restore the green and blue channels by the median underwater dark channel prior (MUDCP) approach. 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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show abstract

“…We carry out experiments comparing our own model, with various state of the art(SOTA) object detector : Faster R-CNN [32] , YOLOX [7], Libra R-CNN [28], Spares R-CNN [37], FCOS [38], DiffusionDet [4], VF [1], pre-processing object detection algorithm like MRSCR [15], GUDCP [20], UWGAN [17], UGAN [5], CWR [9], U-Transformer [29] and underwater object detector: RoiAtt [19], Boosting R-CNN [35], SWIPENet [3], and we use mAP as an evaluation metric for object detection performance. The comparison results of mAP are shown in Table .2.…”

Section: Comparison Of Underwater Image Object Detectionmentioning

confidence: 99%

Shape-Guided Detection: A joint network combining object detection and underwater image enhancement together

Yang,

Jiang,

et al. 2023

Preprint

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Due to the poor quality of underwater images, they are usually not accurate when used for object detection. Most of the currently available solutions involve pre-processing, such as underwater image enhancement, to improve object detection accuracy. However, the pre-processing approach is to improve the subjective perception of the human eye, which does not necessarily improve the object detection performance and also consumes a lot of computational resources. Therefore, in this paper, we innovatively combine these two tasks and propose Shape-Guided Detection Network (SG Detection). We fuse underwater image enhancement and object detection into one network, where object detection can share clean features through joint learning, thus facilitating underwater object detection , and the object detection results will also guide the direction of underwater image enhancement. Meanwhile, in the SG Detection network, we embed the object shape prior features as a learnable module, and design the Shape Priori Enhancement module to achieve the full fusion and interaction between the prior features and local features. In addition, we innovatively adopt a combination of explicit and implicit constraint approach, which ultimately achieves high object detection accuracy. We have conducted extensive experiments on public available datasets, and we find that our approach achieves up to 10% mAP improvement in object detection performance and significantly increases the processing speed.

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“…To improve the quality of underwater images, traditional methods [7], [16]- [20], [51], [58] and deep learning based methods [8], [10]- [13], [23]- [25], [31]- [33], [55], [56], [59] are proposed for underwater image enhancement (UIE). Traditional UIE methods rely on a priori knowledge or assumptions, design rules or models to process underwater images, such as histogram equalization [7] and white balance [9]. Although these methods are simple to implement, the effect is limited and cannot adapt to different water qualities and lighting conditions.…”

Section: Introductionmentioning

confidence: 99%

DiffWater: Underwater Image Enhancement Based on Conditional Denoising Diffusion Probabilistic Model

Guan,

Xu,

Jiang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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GUDCP: Generalization of Underwater Dark Channel Prior for Underwater Image Restoration

Cited by 89 publications

References 28 publications

Passive Visual Underwater Surveillance: A Survey

Passive Visual Underwater Surveillance: A Survey

Shape-Guided Detection: A joint network combining object detection and underwater image enhancement together

DiffWater: Underwater Image Enhancement Based on Conditional Denoising Diffusion Probabilistic Model

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