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An Adaptive Enhancement method for Low Contrast Color Retinal Images based on Strucural Similarity

机译:基于结构相似度的低对比度彩色视网膜图像自适应增强方法

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In this paper, an adaptive luminosity and contrast adjustment technique is proposed for the improved visual perception of low contrast color retinal images. Luminosity improvement is achieved using adaptive gamma correction based on mean structural similarity index maximization performed on the luminosity channel of the original image in Hue, Saturation and Value (HSV) color space. Further, a local contrast enhancement technique is applied on the low frequency component obtained by performing discrete wavelet transform on the enhanced luminosity channel. The experimental results reveal that the image attributes are clearly defined and a better visualization of retinal defects is achieved. Quantitative evaluation based on peak signal to noise ratio, discrete entropy and Structural Similarity Index (SSIM) shows that the proposed method performs better than the existing methods considered. For comparison of results, 128 images from the proprietary database are considered.
机译:本文提出了一种自适应的亮度和对比度调整技术,以改善低对比度彩色视网膜图像的视觉感知。使用基于在色相,饱和度和值(HSV)色彩空间中对原始图像的亮度通道执行的平均结构相似性指数最大化的自适应伽马校正,可以实现亮度改善。此外,将局部对比度增强技术应用于通过对增强的亮度信道执行离散小波变换而获得的低频分量。实验结果表明,清晰地定义了图像属性,并实现了视网膜缺损的更好可视化。基于峰值信噪比,离散熵和结构相似指数(SSIM)的定量评估表明,该方法的性能优于现有方法。为了比较结果,考虑了来自专有数据库的128张图像。

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