A natural-based fusion strategy for underwater image enhancement

Yan, Xiaohong; Wang, Guangxin; Jiang, Guangqi; Mi, Zetian; Fu, Xianping

doi:10.1007/s11042-022-12267-7

Cited by 7 publications

(3 citation statements)

References 56 publications

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“…Yan et al 119 presented a fusion approach for enhancing underwater images. The authors designed an adaptive-white balancing method and obtained two output images based on local and global contrast enhancement.…”

Section: Fusion-based Methodsmentioning

confidence: 99%

Systematic review and analysis on underwater image enhancement methods, datasets, and evaluation metrics

Verma

Kumar

2022

J. Electron. Imag.

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valuable resources that exist beneath Earth's surface. Underwater exploration requires enhanced images that are obtained using enhancement methods. So, it is important that underwater image enhancement (UIE) methods work well in terms of performance and accuracy. As a result, research in UIE has increased in the past few years. An extensive survey is conducted on existing UIE methods along with their broad classification, underwater datasets, and evaluation metrics, respectively. The experimental analysis is conducted to compare the existing UIE methods in terms of qualitative and quantitative evaluation metrics. The real-world applications and future scope of existing enhancement methods are highlighted and discussed.

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Section: Fusion-based Methodsmentioning

confidence: 99%

Systematic review and analysis on underwater image enhancement methods, datasets, and evaluation metrics

Verma

Kumar

2022

J. Electron. Imag.

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“…The fitness value of the white shark can be measured by the objective function. When a more natural input image is obtained, the contrast and information loss is used to estimate the fused image [25]. The contrast and minimum information loss is taken to be the objective functions, defined to be as follows:…”

Section: Fusion Weightsmentioning

confidence: 99%

Adaptive Low Light Image Enhancement Based on Retinex Theory

Guo,

Li,

et al. 2024

Preprint

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Aiming at the color distortion and low visibility in the images with low light conditions, we proposed a low light image enhancement algorithm based on Retinex theory.Firstly, the light component is obtained by multi-scale Gaussian fusion and then are refined. Then, two input images are obtained by the brightness enhancement function and contrast enhancement function, respectively, and the white shark optimization algorithm is applied to perform updating the weights of the Gaussian-Laplace pyramids. Again, the two input images derived from the brightness function and contrast enhancement function are fused by Gaussian-Laplace pyramids. Finally, the final enhanced image are obtained from the multiplication between the adjusted lighting component and the reflection component based on Retinex theory.The experimental results show that the proposed method in this paper has better enhancement effects on images captured under uneven lighting and low lighting conditions.

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“…Yan et al [13] designed an adaptive white balance method by statistical prior to mitigate the impact of color deviation. Huang et al [14] employed a white balance conversion method with Digital SG ColorChecker for color correction.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Color Correction Algorithm for Facial Image

Liu

Wang

Pan

et al. 2022

JAMMR

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Aims: Facial image analysis is the main direction of facial diagnosis objectification, where realistic colors in medical facial images are essential clues for disease diagnosis. However, the same person's facial color information will have different display information under different light sources and display devices, which may lead to different diagnostic results. Methodology: Firstly, we conducted group experiments on the selection of color patches and selected 12 face-related color patches for color correction of facial images. Next, to adapt to this task under different lighting conditions, we utilize an adaptive white balance algorithm to adjust brightness as the first step. We selected the D65 illuminate environment as the standard lighting condition. Finally, we employed a polynomial-regression algorithm based on ridge regression for color correction. Results: The experimental results demonstrate that the average values of the chromatic distance of our method under all five lighting conditions are less than 3.0. We also compare the ablation experiments of adjusting brightness. Conclusion: The results indicate that the proposed method has good consistency in color correction of facial images under different lighting conditions, which is more suitable for clinical medical diagnosis.

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A natural-based fusion strategy for underwater image enhancement

Cited by 7 publications

References 56 publications

Systematic review and analysis on underwater image enhancement methods, datasets, and evaluation metrics

Systematic review and analysis on underwater image enhancement methods, datasets, and evaluation metrics

Adaptive Low Light Image Enhancement Based on Retinex Theory

A Two-Stage Color Correction Algorithm for Facial Image

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