HLIPSCS: A Rapid and Efficient Algorithm for Image Contrast Enhancement

Al-Ameen, Zohair; Younis, Zainab; Al-Ameen, Shamil

doi:10.12785/ijcds/120125

Cited by 5 publications

(2 citation statements)

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“…HE [30] shows color deviation and the image shows localized noise. HLIPSCS [31] overall image does not highlight local details. RCEA [32] correction process introduces undesired white and gray The SIVR [33] enhanced image is whitish overall and the enhancement information is not clear.…”

Section: Qualitative and Quantitative Comparisons On The Test 1 Datasetmentioning

confidence: 98%

“…HE [30] method introduces white tones but at the same time weakens the expression of detailed information. HLIPSCS [31] method enhances the image contrast more consistently but still does not highlight the details of the image. RCEA [32] method enhances the image contrast with white balance distortion, which occurs when the areas in the image are much brighter than the surrounding areas.…”

Section: Qualitative and Quantitative Comparisons On The Test 2 Datasetmentioning

confidence: 99%

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Symptomatic vitreous opacity image enhancement using linear and nonlinear transformations

Zhang,

Ye,

Rao

2024

Preprint

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The rapid development of digital ophthalmology has put forward new demands for symptomatic vitreous opacity image processing, and symptomatic vitreous opacity image enhancement has become a key issue in the research of digital ophthalmology development. Focusing on this key issue, this paper proposes a linear and nonlinear transform-based image enhancement for symptomatic vitreous opacity. Specifically, firstly, a sharpening and white balance correction module is proposed, and the symptomatic vitreous opacity image is sharpened to compensate for the loss of details in the exposed area. Secondly, a visibility restoration module based on type II fuzzy sets is designed. We propose a symptomatic vitreous opacity image enhancement method with interpretability. As a result, the proposed method can solve the exposure and low visibility problems occurring in the symptomatic vitreous opacity image, and produce enhancement results that conform to human visual characteristics and have interpretability. Through comparative experimental analysis, the proposed method achieves superior results compared to advanced image enhancement methods.

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Section: Qualitative and Quantitative Comparisons On The Test 1 Datasetmentioning

confidence: 98%

Section: Qualitative and Quantitative Comparisons On The Test 2 Datasetmentioning

confidence: 99%

Symptomatic vitreous opacity image enhancement using linear and nonlinear transformations

Zhang,

Ye,

Rao

2024

Preprint

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show abstract

A principal component fusion-based thresholded bin-stretching for CT image enhancement

Kumar,

Bhandari

2023

SIViP

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Weighting and thresholding-based detail preserving image enhancement of gastrointestinal images with noise suppression

Abraham

2023

Biomedical Signal Processing and Control

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HLIPSCS: A Rapid and Efficient Algorithm for Image Contrast Enhancement

Cited by 5 publications

References 0 publications

Symptomatic vitreous opacity image enhancement using linear and nonlinear transformations

Symptomatic vitreous opacity image enhancement using linear and nonlinear transformations

A principal component fusion-based thresholded bin-stretching for CT image enhancement

Weighting and thresholding-based detail preserving image enhancement of gastrointestinal images with noise suppression

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