眼科医生广泛使用眼底照相机来监测和诊断视网膜病变。不幸的是,没有一个光学系统是完美的,由于存在有问题的照明,视网膜图像的可见性可能会大大降低,眼内散射,或者由突然的运动引起的模糊。为了提高图像质量,不同的视网膜图像复原/增强技术已经发展,在提高各种临床和计算机辅助应用的性能方面发挥着重要作用。本文对这些修复/增强技术进行了全面的回顾,讨论他们的基本数学模型,并展示了它们如何有效地应用于现实生活中的实践,以提高视网膜图像的视觉质量,用于潜在的临床应用,包括诊断和视网膜结构识别。视网膜图像恢复/增强技术的所有三个主要主题,即,照明校正,去雾,和去模糊,已解决。最后,将讨论一些关于视网膜图像复原/增强技术的挑战和未来范围的考虑。
Fundus cameras are widely used by ophthalmologists for monitoring and diagnosing retinal pathologies. Unfortunately, no optical system is perfect, and the visibility of retinal images can be greatly degraded due to the presence of problematic illumination, intraocular scattering, or blurriness caused by sudden movements. To improve image quality, different retinal image restoration/enhancement techniques have been developed, which play an important role in improving the performance of various clinical and computer-assisted applications. This paper gives a comprehensive
review of these restoration/enhancement techniques, discusses their underlying mathematical models, and shows how they may be effectively applied in real-life practice to increase the visual quality of retinal images for potential clinical applications including diagnosis and retinal structure recognition. All three main topics of retinal image restoration/enhancement techniques, i.e., illumination correction, dehazing, and deblurring, are addressed. Finally, some considerations about challenges and the future scope of retinal image restoration/enhancement techniques will be discussed.