GLAGC: Adaptive Dual-Gamma Function for Image Illumination Perception and Correction in the Wavelet Domain

Yu, Wenyong; Yao, Haiming; Li, Dan; Li, Gangyan; Shi, Hui

doi:10.3390/s21030845

Cited by 15 publications

(8 citation statements)

References 26 publications

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“…When processing color images, if the three components of R, G, and B are processed separately, their correlation will be destroyed, resulting in the problem of color deviation. HSI color model can effectively solve this problem: first, convert the RGB image into the image represented by HSI color space, then keep the chroma component unchanged, only enhance the brightness component, and finally convert it into the image represented by RGB color space [25]. e flow chart of this process is shown in Figure 2.…”

Section: Color Modelmentioning

confidence: 99%

Optimization of Plane Image Color Enhancement Processing Based on Computer Vision Virtual Reality

Xue

Ming

Lian

2022

Mathematical Problems in Engineering

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In order to solve the problems of low brightness contrast of a color image, hiding a large amount of detail information, and deviation of color information in the process of image acquisition, an optimization method of plane image color enhancement processing based on computer vision virtual reality is proposed. In this method, the input RGB image is converted into the image represented by the HSI color model, and its adaptive brightness is adjusted to improve the overall brightness of the image. For the local detail enhancement of the color image, the three-dimensional Gaussian model perceived by retinal neurons is introduced into the illuminance image estimation of the MSR algorithm to enhance the image color. The results are as follows: from the perspective of objective parameter evaluation, the mean, standard deviation, information entropy, and average gradient of example images 1 and 2 are improved by about 70%; this algorithm not only enhances the brightness and contrast of the image but also maintains the detailed edge information of the image and the color characteristics of the object itself. The average enhancement rate is the highest among various algorithms, up to 95%. The algorithm proposed in this paper maintains the edge detail information of the image, optimizes the defects of the combination of traditional bilateral filtering and Retinex algorithm, and the color is also well restored, which makes the monitoring image easier to identify, more conducive to criminal investigation and solving cases, and lays a foundation for subsequent image processing.

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Section: Color Modelmentioning

confidence: 99%

Optimization of Plane Image Color Enhancement Processing Based on Computer Vision Virtual Reality

Xue

Ming

Lian

2022

Mathematical Problems in Engineering

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“…Wang Wencheng [22] proposed an adaptive gamma transform calculated after linear stretching of the corrected brightness in the image. Alternately, the discrete wavelet transform is utilized by Wenyung Yu et al [23] for gamma correction. The illumination was extracted from a low-light image using wavelet decomposition.…”

Section: Related Workmentioning

confidence: 99%

EAGL: Enhancement Algorithm based on Gamma Correction for Low Visibility Images

Rekhi¹,

Sidhu²

2022

IJACSA

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Under poor light conditions or improper acquisition settings, the image degrades due to low contrast, poor brightness and suffer poor visual quality of the picture. An enhancement is required to manipulate the scale of pixel intensity for significant improvement in the image. This paper proposed the method of gamma correction with a self-adaptive value in accordance with the intensity scale of the image. After transformation to HSI (hue, saturation and intensity) channel, a multi-scale wavelet transform is implemented on the intensity component of the image. The gamma scale is computed from the combination of reformed scale constant of logarithm function and Minkowski distance measure. Lastly, wavelet based denoising technique is applied to suppress high noise coefficients to improve quality of the image. The proposed method is evaluated in terms of visual appearance, measure of information content, signal to noise ratio, and universal image quality index. It demonstrated that the proposed method showed its efficacy in terms of quality and improved visibility.

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“…Non-linear transfer function such as the gamma correction is applied to increase the image quality by contrast enhancement and preserving of mean luminance [10]. Nonetheless, the selection of gamma correction settings is a time-consuming manual process [17]. To improve the contrast of an image, an adaptive gamma correction (AGC) method is suggested in which the appropriate gamma value is determined automatically using image statistics [18].…”

Section: Figurementioning

confidence: 99%

“…Each enhancement method's output image will be analyzed using a Blind/Reference-less Image Spatial Quality Evaluator (BRISQUE). BRISQUE is a referenceless quality assessment technique [16,17]. To some extent, it is a comprehensive evaluation metric that makes use of 'natural scene statistics' to evaluate both the lack of 'naturalness' in an image and the presence of distortions.…”

Section: Blind/referenceless Image Spatial Quality Evaluator (Brisque)mentioning

confidence: 99%

Multi-temporal satellite image correction based on CNN features and adaptive contrast enhancement

Saberi

Hashim

Ali

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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In recent years, identifying changes in multi-temporal images in terms of land use and land cover has been significant in a variety of applications, including urban planning. Due to weather and environmental effects, optical remote sensing has limitations in obtaining images where the image quality may be degraded. It’s because the images being registered are taken at various times, viewpoints, and types of sensors. In this article, the pre-processing methods, which include radiometric correction and geometric correction, are introduced to enhance the quality of satellite images and identify correct spatial alignment. For radiometric correction, adaptive contrast enhancement is done by combining histogram- and non-linear transfer function-based approaches in CIELAB color space. A comparison study is done to see how the new method compared to other methods. For geometric correction, the features from two images are extracted using Convolutional Neural Network to match and align them. The introduced approach for radiometric correction gave the best average rank of BRISQUE scores and RMSE of contrast scores, and the geometric correction can align two images together with an average accuracy of improvement of 91.78 percent. The findings of this research will provide the preliminary step for any change detection activities.

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GLAGC: Adaptive Dual-Gamma Function for Image Illumination Perception and Correction in the Wavelet Domain

Cited by 15 publications

References 26 publications

Optimization of Plane Image Color Enhancement Processing Based on Computer Vision Virtual Reality

Optimization of Plane Image Color Enhancement Processing Based on Computer Vision Virtual Reality

EAGL: Enhancement Algorithm based on Gamma Correction for Low Visibility Images

Multi-temporal satellite image correction based on CNN features and adaptive contrast enhancement

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