An Image Based Technique for Enhancement of Underwater Images

Prabhakar, C. J.; Kumar, Pavan

doi:10.48550/arxiv.1212.0291

Cited by 13 publications

(2 citation statements)

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“…Spatial domain methods such as histogram equalization (Singh and Kapoor, 2014)and gray world hypothesis algorithm (Buchsbaum, 1980), and frequency domain methods such as Fourier transform (Prabhakar and Praveen Kumar, 2011) and wavelet transform (Singh et al, 2015) are traditional methods. These methods can handle noises better but often bring problems of low contrast, loss of details, and color deviation.…”

Section: Underwater Image Enhancement Methodsmentioning

confidence: 99%

An underwater image enhancement model for domain adaptation

Deng

Liu

et al. 2023

Front. Mar. Sci.

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Underwater imaging has been suffering from color imbalance, low contrast, and low-light environment due to strong spectral attenuation of light in the water. Owing to its complex physical imaging mechanism, enhancing the underwater imaging quality based on the deep learning method has been well-developed recently. However, individual studies use different underwater image datasets, leading to low generalization ability in other water conditions. To solve this domain adaptation problem, this paper proposes an underwater image enhancement scheme that combines individually degraded images and publicly available datasets for domain adaptation. Firstly, an underwater dataset fitting model (UDFM) is proposed to merge the individual localized and publicly available degraded datasets into a combined degraded one. Then an underwater image enhancement model (UIEM) is developed base on the combined degraded and open available clear image pairs dataset. The experiment proves that clear images can be recovered by only collecting the degraded images at some specific sea area. Thus, by use of the scheme in this study, the domain adaptation problem could be solved with the increase of underwater images collected at various sea areas. Also, the generalization ability of the underwater image enhancement model is supposed to become more robust. The code is available at https://github.com/fanren5599/UIEM.

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Section: Underwater Image Enhancement Methodsmentioning

confidence: 99%

An underwater image enhancement model for domain adaptation

Deng

Liu

et al. 2023

Front. Mar. Sci.

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“…Preprocessing of underwater photos has been extensively researched, and several solutions have been devised for correcting typical underwater image artefacts [95,96]. However, the image quality produced by these approaches is subjective to the observer, and because acquisition settings vary so widely, these methods may not be applicable to all datasets.…”

Section: Image Qualitymentioning

confidence: 99%

Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey

Saleh,

Sheaves,

Azghadi

2022

Preprint

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Marine scientists use remote underwater video recording to survey fish species in their natural habitats. This helps them understand and predict how fish respond to climate change, habitat degradation, and fishing pressure. This information is essential for developing sustainable fisheries for human consumption, and for preserving the environment. However, the enormous volume of collected videos makes extracting useful information a daunting and time-consuming task for a human. A promising method to address this problem is the cutting-edge Deep Learning (DL) technology.DL can help marine scientists parse large volumes of video promptly and efficiently, unlocking niche information that cannot be obtained using conventional manual monitoring methods. In this paper, we provide an overview of the key concepts of DL, while presenting a survey of literature on fish habitat monitoring with a focus on underwater fish classification. We also discuss the main challenges faced when developing DL for underwater image processing and propose approaches to address them. Finally, we provide insights into the marine habitat monitoring research domain and shed light on what the future of DL for underwater image processing may hold. This paper aims to inform a wide range of readers from marine scientists who would like to apply DL in their research to computer scientists who would like to survey state-of-the-art DL-based underwater fish habitat monitoring literature.

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