人工智能
颜色恒定性
计算机科学
计算机视觉
照度
对比度(视觉)
自然性
直方图均衡化
视网膜
工件(错误)
RGB颜色模型
频道(广播)
小学生
直方图
光学
图像(数学)
医学
眼科
物理
量子力学
计算机网络
作者
Rui Han,Chen Tang,Min Xu,Bingtao Liang,Tianbo Wu,Zhenkun Lei
摘要
Retinal images are widely used for the diagnosis of various diseases. However, low-quality retinal images with uneven illumination, low contrast, or blurring may seriously interfere with diagnosis by ophthalmologists. This study proposes an enhancement method for low-quality retinal color images. In this paper, an improved variational Retinex model for color retinal images is first proposed and applied to each channel of the RGB color space to obtain the illuminance and reflectance layers. Subsequently, the Naka–Rushton equation is introduced to correct the illumination layer, and an enhancement operator is constructed to improve the clarity of the reflectance layer. Finally, the corrected illuminance and enhanced reflectance are recombined. Contrast-limited adaptive histogram equalization is introduced to further improve the clarity and contrast. To demonstrate the effectiveness of the proposed method, this method is tested on 527 images from four publicly available datasets and 40 local clinical images from Tianjin Eye Hospital (China). Experimental results show that the proposed method outperforms the other four enhancement methods and has obvious advantages in naturalness preservation and artifact suppression.
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