Fundus images are broadly used by medical ophthalmologists to detect and assess any customary abnormalities. Fundus imaging sensors capture the eye's rigid portion, which characteristically covers the core, tangential retina, optic disc, and macula. Existing state‐of‐the‐art fundus sensors have the drawback of producing low contrast and noisy information, which makes scientific and algorithmic evaluation very complicated. This article proposes an Adaptive Histogram Equalization—Tuned with Nonsimilar Grouping Curvelet (HET‐NOSCU), which works through a joint denoising enhancement approach. The proposed algorithm's main contribution includes (i) use of curvelet features to better preserve edges during denoising. (ii) Adaptive enhancement using the histogram to prevent halo ringing and specular artifacts, which yields superior results than the very recently established state‐of ‐the‐art methods, using similar performing parameters such as peak signal to noise ratio (PSNR), structural similarity index (SSIM), and correlation coefficient (CoC). We observe an improvement of 17.66%, 0.93%, and 0.24%, respectively, for the above parameters.

中文翻译：

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使用非相似分组Curvelet调整的自适应直方图均衡对视网膜图像进行增强

眼底图像被医学眼科医生广泛用于检测和评估任何常规异常。眼底成像传感器捕获眼睛的刚性部分，该部分典型地覆盖了核心，切向视网膜，视盘和黄斑。现有的最先进的眼底传感器具有产生低对比度和嘈杂信息的缺点，这使得科学和算法评估非常复杂。本文提出了一种自适应直方图均衡化-通过非相似分组曲率（HET-NOSCU）进行调整，该方法通过联合降噪增强方法来工作。提出的算法的主要贡献包括（i）使用Curvelet特征在去噪过程中更好地保​​留边缘。（ii）使用直方图进行自适应增强，以防止晕圈和镜面反射伪影，使用相似的执行参数，例如峰值信噪比（PSNR），结构相似性指数（SSIM）和相关系数（CoC），与最近建立的最新方法相比，该方法产生了更好的结果。对于上述参数，我们分别观察到了17.66％，0.93％和0.24％的改进。