Open AccessRetinal Image Enhancement Using Curvelet Based Sigmoid Mapping of Histogram Equalization
Author(s) -
Abubakar Bala,
P. Aruna Priya,
Vivek Maik
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1964/6/062034
Subject(s) - artificial intelligence , adaptive histogram equalization , computer vision , computer science , histogram equalization , fundus (uterus) , peak signal to noise ratio , noise reduction , curvelet , image quality , histogram , pattern recognition (psychology) , image (mathematics) , wavelet transform , wavelet , medicine , ophthalmology
Ophthalmologists generally use retinal fundus images to identify certain retinal diseases. However, fundus cameras frequently fail to capture high-quality retinal images due to improper camera settings, eye movement, uneven illumination, and pupil dilation that affect the diagnosis’s reliability. To enhance the fundus image’s visual clarity, we propose a combination of denoising and enhancement methods. This paper uses a multi-resolution curvelet transform and adaptive sigmoid mapping of histogram equalization for better image denoising and enhancement. Our hybrid technique enhances the quality of fundus image with improvement in Peak Signal to Noise Ratio (PSNR) of 6.85%, Structural Similarity Index (SSIM) of 0.89%, and Correlation coefficient (CoC) of 0.13%compared to existing methods with Gaussian noise of about 0.01.