Gray world based color correction and intensity preservation for image enhancement

Kwok, Ngaiming; Wang, D.; Jia, Xiuping; Chen, S.Y.; Ha, Q.P.

doi:10.1109/cisp.2011.6100336

Cited by 21 publications

(9 citation statements)

References 15 publications

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“…In the gray world color correction, one of the color channels is selected as a reference to calculate the illuminant but the intensity of the resultant normalized image degrades and may hinder the diagnostic results. Therefore, in the proposed method, a compressed color channel technique of the Gray world algorithm is used in which the color channel with minimum average magnitude is selected as proposed in [32].…”

Section: Step 2: Gray World Illuminant Calculationmentioning

confidence: 99%

Diabetic Retinopathy Diagnosis From Fundus Images Using Stacked Generalization of Deep Models

et al. 2021

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Diabetic retinopathy (DR) is a diabetes complication that affects the eye and can cause damage from mild vision problems to complete blindness. It has been observed that the eye fundus images show various kinds of color aberrations and irrelevant illuminations, which degrade the diagnostic analysis and may hinder the results. In this research, we present a methodology to eliminate these unnecessary reflectance properties of the images using a novel image processing schema and a stacked deep learning technique for the diagnosis. For the luminosity normalization of the image, the gray world color constancy algorithm is implemented which does image desaturation and improves the overall image quality. The effectiveness of the proposed image enhancement technique is evaluated based on the peak signal to noise ratio (PSNR) and mean squared error (MSE) of the normalized image. To develop a deep learning based computer-aided diagnostic system, we present a novel methodology of stacked generalization of convolution neural networks (CNN). Three custom CNN model weights are fed on the top of a single meta-learner classifier, which combines the most optimum weights of the three sub-neural networks to obtain superior metrics of evaluation and robust prediction results. The proposed stacked model reports an overall test accuracy of 97.92% (binary classification) and 87.45% (multi-class classification). Extensive experimental results in terms of accuracy, F-measure, sensitivity, specificity, recall and precision reveal that the proposed methodology of illumination normalization greatly facilitated the deep learning model and yields better results than various state-of-art techniques.

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Section: Step 2: Gray World Illuminant Calculationmentioning

confidence: 99%

Diabetic Retinopathy Diagnosis From Fundus Images Using Stacked Generalization of Deep Models

et al. 2021

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“…GW method has been widely used in atmospheric images [26] to remove color cast. In some cases, after the removal of the color cast, color correction is applied [27]. In the underwater image enhancement method, Bianco et al [28] proposed a color correction method for underwater imaging in the Ruderman opponent color space lα β that based on GW and uniform illumination assumptions.…”

Section: Related Workmentioning

confidence: 99%

An Integrated Method to Remove Color Cast and Contrast Enhancement for Underwater Image

Wong

Raveendran

Yoshida

et al. 2019

IEICE Trans. Fundamentals

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Light scattering and absorption of light in water cause underwater images to be poorly contrasted, haze and dominated by a single color cast. A solution to this is to find methods to improve the quality of the image that eventually leads to better visualization. We propose an integrated approach using Adaptive Gray World (AGW) and Differential Gray-Levels Histogram Equalization for Color Images (DHECI) to remove the color cast as well as improve the contrast and colorfulness of the underwater image. The AGW is an adaptive version of the GW method where apart from computing the global mean, the local mean of each channel of an image is taken into consideration and both are weighted before combining them. It is applied to remove the color cast, thereafter the DHECI is used to improve the contrast and colorfulness of the underwater image. The results of the proposed method are compared with seven state-of-the-art methods using qualitative and quantitative measures. The experimental results showed that in most cases the proposed method produced better quantitative scores than the compared methods.

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“…14 In nonreferencing corrections, the white-point assumption is frequently employed. 15 Here, we propose an alternative assumption that there is at least one color of one pixel whose magnitude is zero, while at least one pixel whose maximum color magnitude is unity. This assumption leads to stretching the magnitude of each color elements of a pixel to span the complete magnitude range.…”

Section: Min-max Alignment and Magnitude Sortingmentioning

confidence: 99%

Gradient-guided color image contrast and saturation enhancement

Shi

Kwok

Lin

et al. 2017

International Journal of Advanced Robotic Systems

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Digital color images are capable of presenting hue, saturation, and brightness perceptions. Therefore, quality improvement of color images should be taken into account to enhance all three stimuli. An effective method is proposed that aims at enriching the colorfulness, vividness, and contrast of color images simultaneously. In this method, color correction based on magnitude stretching is carried out first, image enhancement is then derived from an intensity-guided operation that concurrently improves the contrast and saturation qualities. Furthermore, the proposed methodology mitigates the heavy computational burden arising from the need to transform the source color space into an alternative color space in conventional approaches. Experiments had been conducted using a collection of real-world images captured under various environmental conditions. Image quality improvements were observed both from subjective viewing and quantitative evaluation metrics in colorfulness, saturation, and contrast.

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Gray world based color correction and intensity preservation for image enhancement

Cited by 21 publications

References 15 publications

Diabetic Retinopathy Diagnosis From Fundus Images Using Stacked Generalization of Deep Models

Diabetic Retinopathy Diagnosis From Fundus Images Using Stacked Generalization of Deep Models

An Integrated Method to Remove Color Cast and Contrast Enhancement for Underwater Image

Gradient-guided color image contrast and saturation enhancement

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