Color Correction Based on CFA and Enhancement Based on Retinex With Dense Pixels for Underwater Images

Li, Changli; Tang, Shiqiang; Kwan, Hon Keung; Yan, Jingwen; Zhou, Teng

doi:10.1109/access.2020.3019354

Cited by 33 publications

(9 citation statements)

References 56 publications

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“…Later, the Improved UWB (IUWB) methods [7] improved the color compensation based on the standard deviation ratio and proposed a mean adjustment method to ensure the means of the blue and red channels were equivalent to that of the green channel. Besides, the Color Filter Array (CFA) based method [6] proposed to compensate the red channel based on the blue and green channels. Other than the Red, Green, Blue (RGB) channel compensation, the Color Channel Transfer (CCT) method [3] proposed to recompense the International Commission on Illumination L*a*b* (CIELab) channels through the color transfer due to the observation that D N L Yao 1* , A Bade 1 , and Z Waheed 2…”

Section: Literature Reviewmentioning

confidence: 99%

Recompense the Color Loss for Underwater Image using Generalized Color Compensation (GCC) Technique

Yao

Bade

Waheed

2022

J. Phys.: Conf. Ser.

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The light absorption of the water medium often caused the underwater image enhancement to become challenging due to the loss in color channels where the red color vanished faster than others. The color loss often affected the underwater image quality especially having the color cast like greenish, bluish, and yellowish. Therefore, this paper introduced a Generalized Color Compensation (GCC) technique which will recompense the color loss based on maximum channel and then remove the color cast using the mean adjustment. For the experiments conducted, the GCC technique improved the image contrast successfully. The experiments showed that the proposed technique was able to remove the color cast effectively regardless of greenish, bluish, and yellowish and improve the underwater image quality.

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Section: Literature Reviewmentioning

confidence: 99%

Recompense the Color Loss for Underwater Image using Generalized Color Compensation (GCC) Technique

Yao

Bade

Waheed

2022

J. Phys.: Conf. Ser.

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“…It is an ideal choice for human eyes to enhance the overall saturation and color brightness of images. Compared with the enhanced images of different enhancement algorithms, there are varying degrees of shift in the reference method [4] and the reference method [16], and yellow shift in the reference method [17], which indicates that these algorithms have poor color correction effects on underwater images with different depth of field. There is obvious blue deviation in C of the enhancement results of reference method [2].…”

Section: 1subjective Evaluationmentioning

confidence: 99%

“…However, due to the change of underwater environment, different underwater images have different characteristics and have certain shortcomings. For example, Fu's method is too simple, Chen's method cannot distinguish different underwater backgrounds caused by different depth of field [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

An underwater image enhancement method based on color correction and local unsharp masking

Yang

Yun

2022

J. Phys.: Conf. Ser.

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The underwater images collected by optical cameras have different problems due to the camera equipment, underwater environment and light source, which lead to the problems of chromatic aberration and low contrast of the images collected by ordinary people for subsequent use. The reasons are that the reflection of light by impurities in the water and the absorption efficiency of different wavelengths of light under water, and under different depths of field conditions often leads to color Bias and contrast are low. To solve this problem, we adopt a method based on color correction and local unsharp masking for the fusion of underwater images. Firstly, color is improved by correcting for color shift using the red channel and gray world methods, combined with CLAHE. secondly, for the low contrast problem, the adaptive mask coefficient and the local unsharp mask are used to separate the background and foreground, which enhances the recognition of underwater target and background, and effectively improves the contrast of images. Target. Finally, the weighted fusion of Gaussian pyramid is completed by using the method of combining four weights. The experimental results show that the algorithm proposed in this paper has a strong generalization ability, and can improve the quality of images in different underwater scenes which is according to different objective evaluation indicators, the algorithm in this paper also achieves the best results.

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“…In 2020, Changli Li et al [2], presented a new color correction approach based on CFA. Moreover, they had also presented a developed approach on the basis of the Retinex with dense pixels as well as the transformation of the adaptive linear histogram for degraded color-biased UI.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, the valuable information extraction for underwater scenes needs effectual techniques to rectify the color, enhance the clarity and identify blurring as well as background scattering that is the aspire of image restoration as well as enhancement techniques [8], [9]. Because of the complex underwater environment, these are specifically confronted, wherein degrading of images are occurred by the manipulation of absorption of light, water turbidity, as well as scattering that might alter extensively [2].…”

Section: Introductionmentioning

confidence: 99%

Underwater Image Enhancement using Improved Bat Algorithm

Deshmukh¹

2021

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From the past decades, improving the superiority of an Underwater Image (UI) has received well recognition in terms of the deprived image visibility that is occurred due to the water medium physical property. The light absorption of wavelength-reliant, as well as scattering in an underwater view, degrades the image visibility which causes lesser contrast as well as distorted color casts. To overcome the issues, a highly developed technique is adopted for the advancement and it improves the UI classification. Specifically, by exploiting the Improved Bat Algorithm (IBA), the underwater input image RGB is enhanced. The next stage is feature extraction. To the enhanced Principle Component Analysis (PCA) technique, the extracted attributes are given as input; here the dimensions of the features are minimized. Subsequently, the classification operation is carried out by exploiting the ANFIS classifier. Finally, the classified improved deeper water images besides the improved shallow water images which are seen are available in the testing stage. Finally, the simulation outcomes for the proposed and conventional methods are analyzed. The developed UIE system shows superior accuracy while compared with the conventional techniques.

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Color Correction Based on CFA and Enhancement Based on Retinex With Dense Pixels for Underwater Images

Cited by 33 publications

References 56 publications

Recompense the Color Loss for Underwater Image using Generalized Color Compensation (GCC) Technique

Recompense the Color Loss for Underwater Image using Generalized Color Compensation (GCC) Technique

An underwater image enhancement method based on color correction and local unsharp masking

Underwater Image Enhancement using Improved Bat Algorithm

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