Underwater Image Quality: Enhancement and Evaluation

Mohamed, Nadir Mustafa A.; Lin, Liqun; Chen, Weiling; Wei, Hongan

doi:10.1109/csrswtc50769.2020.9372502

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…It does not, however, improve contrast or do colour correction which causes this method receiving less attention. Moreover, spatial domain-based method such as Gamma Correction, Generalized Unsharp Masking (GUM), Histogram Equalization (HE), and Contrast Limited Adaptive Histogram Equalization (CLAHE) made great progress in the field of image enhancement since it is mature and simple to adopt, and it has been improved to combat the deterioration of the underwater image [4]. This method typically demands the employment of colour correction and noise reduction techniques in combination.…”

Section: Underwater Image Enhancement Methodsmentioning

confidence: 99%

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Comparison of Deep Learning Methods for Underwater Image Enhancement

Jobli¹,

Hashim²

2022

Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)

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Underwater image enhancement is an important process in image processing due to the images often suffering from severe degradation causes by the nature of light and underwater environment. The purpose of this research is to study the existing methods and algorithms for enhancing underwater images. In this paper, we compared 3 different deep learning-based methods (i.e. Water-Net, Shallow-UWnet, Deep Learning and Image Formation Model) for underwater image enhancement. Furthermore, we proposed an enhancement method based on white balance, adaptive gamma correction, sharpening and multi-scale fusion technique. The result of our proposed method is fed into the deep learning-based models. A real-world dataset which is the Underwater Image Enhancement Benchmark (UIEB) dataset is used for the model training and testing. Experimental results show that our proposed method improves the colour hue, image clarity and achieves higher scores in terms PSNR, SSIM and UIQM metrics.

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Section: Underwater Image Enhancement Methodsmentioning

confidence: 99%

“…Images are subjected to repeated observation experiments by a group of testers. While qualitative evaluation is important as a baseline, it is time-consuming and difficult to implement into practical applications [4]. As a result, quantitative evaluation is more widely accepted and reliable.…”

Section: Underwater Image Quality Evaluationmentioning

confidence: 99%

Comparison of Deep Learning Methods for Underwater Image Enhancement

Jobli¹,

Hashim²

2022

Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)

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show abstract

“…Consequently, these will cause great trouble in feature extraction, target detection and tracking. Therefore, improving the clarity of underwater images is very important to support the optical detection techniques in close range operations of underwater vehicles [4].…”

Section: Introductionmentioning

confidence: 99%

Underwater image clarifying based on human visual colour constancy using double‐opponency

Kong

Qian

Song

et al. 2023

CAAI Trans on Intel Tech

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Underwater images are often with biased colours and reduced contrast because of the absorption and scattering effects when light propagates in water. Such images with degradation cannot meet the needs of underwater operations. The main problem in classic underwater image restoration or enhancement methods is that they consume long calculation time, and often, the colour or contrast of the result images is still unsatisfied. Instead of using the complicated physical model of underwater imaging degradation, we propose a new method to deal with underwater images by imitating the colour constancy mechanism of human vision using double‐opponency. Firstly, the original image is converted to the LMS space. Then the signals are linearly combined, and Gaussian convolutions are performed to imitate the function of receptive fields (RFs). Next, two RFs with different sizes work together to constitute the double‐opponency response. Finally, the underwater light is estimated to correct the colours in the image. Further contrast stretching on the luminance is optional. Experiments show that the proposed method can obtain clarified underwater images with higher quality than before, and it spends significantly less time cost compared to other previously published typical methods.

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Research on Global Contrast Calculation Considering Color Differences

Qian

Kong

2021

Image and Graphics Technologies and Applications

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Underwater Image Quality: Enhancement and Evaluation

Cited by 6 publications

References 10 publications

Comparison of Deep Learning Methods for Underwater Image Enhancement

Comparison of Deep Learning Methods for Underwater Image Enhancement

Underwater image clarifying based on human visual colour constancy using double‐opponency

Research on Global Contrast Calculation Considering Color Differences

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