发表机构
Independent Researcher, Department of Information and Computer Engineering, Scottsdale, AZ, USA; Seidenberg School of Computer Science and Information Systems, Pace University, New York City, NY, USA（独立研究者，信息与计算机工程系，斯科茨代尔，亚利桑那州，美国; 塞登堡计算机科学与信息系统学院，帕克大学，纽约市，纽约州，美国）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对视网膜眼底图像因光照等问题影响诊断的情况，提出结合HSV颜色空间分解亮度校正与CLAHE的两阶段图像增强流程，经实验验证其在对比度和结构保真度上优于传统方法，处理速度适配临床筛查。
AI 中文摘要
背景：视网膜眼底成像对于糖尿病性视网膜病变、青光眼和视网膜静脉阻塞等威胁视力疾病的早期诊断至关重要。眼底图像的临床效用常因光照不均匀、运动模糊和低对比度等伪像而受损，增加了诊断错误风险。有效的图像增强是可靠的计算机辅助眼科诊断的前提。方法：本研究提出一种两阶段图像增强流程，通过HSV颜色空间分解进行亮度校正，并将对比度受限自适应直方图均衡化（CLAHE）专门应用于亮度（V）通道。在公开的DRIVE数据集（40张视网膜眼底图像，5８4×565像素，佳能CR5相机，眼科医生标注的地面真值）上进行实验。定量评估采用峰值信噪比（PSNR）、结构相似性指数（SSIM）和对比度噪声比（CNR）。基线比较包括标准直方图均衡化（HE）和自适应直方图均衡化（AHE）。随后应用二元掩蔽步骤来分离与血管病变一致的高反射区域。结果：所提方法实现了PSNR = 29.3 dB、SSIM = 0.91和CNR = 3.12，在所有指标上均优于HE（PSNR = 21.4 dB，SSIM = 0.74）和AHE（PSNR = 23.1 dB，SSIM = 0.79），平均每张图像处理时间为0.14秒。结论：与已有的基线方法相比，亮度-CLAHE组合流程在对比度和结构保真度方面有显著提升，处理速度与临床筛查工作流程兼容。讨论了基于深度学习比较的局限性和方向。
英文摘要
Background: Retinal fundus imaging is central to the early diagnosis of sight-threatening conditions including diabetic retinopathy, glaucoma, and retinal vein occlusion. Clinical utility of fundus images is routinely compromised by non-uniform illumination, motion blur, and low contrast - artefacts that increase the risk of diagnostic error. Effective image enhancement is therefore a prerequisite for reliable computer-aided ophthalmic diagnosis. Methods: This study proposes a two-stage image enhancement pipeline combining luminosity correction via HSV colour space decomposition with Contrast Limited Adaptive Histogram Equalization (CLAHE) applied exclusively to the Value (V) channel. Experiments are conducted on the publicly available DRIVE dataset (40 retinal fundus images, 584 x 565 pixels, Canon CR5 camera, ophthalmologist-annotated ground truth). Quantitative evaluation employs Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Contrast-to-Noise Ratio (CNR). Baseline comparisons include standard Histogram Equalization (HE) and Adaptive Histogram Equalization (AHE). A binary masking step is subsequently applied to isolate hyper-reflective regions consistent with vascular pathology. Results: The proposed method achieves PSNR = 29.3 dB, SSIM = 0.91, and CNR = 3.12 - outperforming HE (PSNR = 21.4 dB, SSIM = 0.74) and AHE (PSNR = 23.1 dB, SSIM = 0.79) across all metrics, with an average processing time of 0.14 seconds per image. Conclusions: The combined luminosity-CLAHE pipeline yields measurably superior contrast and structural fidelity compared to established baseline methods, with processing speed compatible with clinical screening workflows. Limitations and directions for deep-learning-based comparison are discussed.
Comments11 pages
Journal refJournal of Intelligent Medicine and Healthcare 2026, 4, 87-97
