Principal Component Analysis-Based Low-Light Image Enhancement Using Reflection Model

Singh, Neha; Bhandari, Ashish Kumar

doi:10.1109/tim.2021.3096266

Cited by 47 publications

(18 citation statements)

References 31 publications

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“…Hence, the method could not be used for low-intensity images. The improved approach of principal component analysis was suggested by Singh and Bhandari [16]. The authors implemented PCA after the transformation of raw image into HSV channel.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, algorithms developed with gamma correction had been the thrust area in image enhancement. The improved gamma correction was developed by Veluchamy and Subramani [16] and computed from cumulative density function. In this method, the detail information was preserved while the contrast was improved.…”

Section: Related Workmentioning

confidence: 99%

“…The contrast value was improved with the computation of global and local spatial illumination. The recent approach for visual correction was proposed with the implementation of principal component analysis (PCA) by Singh and Bhandari [16]. In this approach principal components are adjusted to compute the gamma scale.…”

Section: Related Workmentioning

confidence: 99%

“…But, however, such metrics quantify the distortion content in the image. As a result, in various research articles [13], [16], [27], different objective assessment is used to evaluate final image. The quality measure is based on full reference methods and non-referenced methods dependant on reference image.…”

Section: A Image Quality Assessment Measure (Iqa)mentioning

confidence: 99%

“…The principal component analysis is a dimensionality reduction technique to produce a scaling in brightness. But implementing principal component analysis (PCARM) [16], the loss of details could be visualized near the swan (shadow at the ground) and failed to preserve the variation of shadow to light region. The fusion method [27] lacked sharpness and failed to preserve the detailed information.…”

Section: E Comparison Of Enhancement Algorithm With Eaglmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: A Image Quality Assessment Measure (Iqa)mentioning

confidence: 99%

Section: E Comparison Of Enhancement Algorithm With Eaglmentioning

confidence: 99%

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

Rekhi¹,

Sidhu²

2022

IJACSA

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Bilateral tone mapping scheme for color correction and contrast adjustment in nearly invisible medical images

Subramani

Veluchamy

2023

Color Research & Application

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Medical images are commonly used to diagnose and screen various diseases. However, medical images taken by operators with different skill levels have a considerable variation in visual quality. Poor illumination of images increases uncertainty in clinical treatment and lead to the risk of misdiagnosis. This paper proposes a novel bilateral tone‐mapped gamma correction to enhance the visual quality of medical images without affecting the natural color quality. First, the proposed tone‐mapped gamma correction is employed to enhance the intensity values of poorly illuminated medical images. Then, bilateral enhancement curve adjustment is performed to effectively improve the global luminance content of the input image with features and richer information. The image quality assessment measures like entropy, blind/reference less image spatial quality evaluator, natural image quality evaluator, perception‐based image quality evaluator, contrast enhancement based image quality parameter, and color contrast fog density evaluates the superior performance of the proposed technique. The experimental results significantly enhance the intensity difference between dark and bright areas and can aid clinical experts and intelligent health fields.

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