Retinal Image Enhancement Using Curvelet Based Sigmoid Mapping of Histogram Equalization
Retinal Image Enhancement Using Curvelet Based Sigmoid Mapping of Histogram Equalization
复制标题
使用基于直方图均衡的曲波 S 型映射的视网膜图像增强
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
V. Maik
中科院分区：
文献类型：
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作者：
A. Bala;Aruna Priya;V. Maik
眼科医生通常使用视网膜眼底图像来识别某些视网膜疾病。然而，眼底照相机经常由于照相机设置不当、眼球运动、照明不均匀和瞳孔扩张而无法捕获高质量的视网膜图像，这些因素影响了诊断的可靠性。为了增强眼底图像的视觉清晰度，本文提出了一种去噪和增强相结合的方法。本文采用多分辨率曲波变换和自适应sigmoid映射直方图均衡化方法对图像进行去噪和增强。我们的混合技术提高了眼底图像的质量，峰值信噪比（PSNR）为6.85%，结构相似性指数（SSIM）为0.89%，相关系数（CoC）为0.13%，与现有的方法相比，高斯噪声约为0.01。
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.