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An Effective Image Enhancement Method for Color Fundus Images

Authors:

JiaZhen HeAuthors Info & Claims

ACAI '21: Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

Article No.: 43, Pages 1 - 5

https://doi.org/10.1145/3508546.3508589

Published: 25 February 2022 Publication History

Abstract

A method is proposed for adaptive fundus image enhancement so as to restore the color images that are with extremely low or nonuniform brightness. Our proposed method is capable of increasing the brightness, contrast of images without losing original color hue. It includes three steps: luminance enhancement, contrast improvement, and color restoration. All procedures are adapted and modified from conventional image processing algorithms, which are proved simple and less time-consuming. Two public and commonly used fundus image database were partially employed to evaluate the performance of our method. It is concluded that the brightness of images was enhanced by 86.35 % and the contrast was improved by 132.37 % without color distortion.

References

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Cited By

Wang YXv ZWang YJin KGao ZKe YWu CChen XChen DYe JJia G(2024)BathNet: A network to classification of fundus and contrast images using label transfer and multi-branch transformerBiomedical Signal Processing and Control10.1016/j.bspc.2024.10640995(106409)Online publication date: Sep-2024
https://doi.org/10.1016/j.bspc.2024.106409

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ACAI '21: Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

December 2021

699 pages

ISBN:9781450385053

DOI:10.1145/3508546

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 February 2022

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ACAI'21

ACAI'21: 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

December 22 - 24, 2021

Sanya, China

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Cited By

Wang YXv ZWang YJin KGao ZKe YWu CChen XChen DYe JJia G(2024)BathNet: A network to classification of fundus and contrast images using label transfer and multi-branch transformerBiomedical Signal Processing and Control10.1016/j.bspc.2024.10640995(106409)Online publication date: Sep-2024
https://doi.org/10.1016/j.bspc.2024.106409

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