针对眼底图像亮度不均问题，提出双阶段增强方法提升诊断清晰度。
Luminosity-Adaptive Contrast Enhancement Using CLAHE for Retinal Fundus Images with Quantitative Validation and Comparative Analysis
- 先在HSV空间分离亮度，再对明度通道用CLAHE增强对比度
- 在DRIVE数据集上达到PSNR 29.3dB、SSIM 0.91、CNR 3.12
- 处理速度快至每图0.14秒，适合临床筛查场景
眼底成像对糖尿病视网膜病变、青光眼等眼病的早期诊断至关重要。但图像常因光照不均、运动模糊和低对比度导致诊断误差。本研究提出一种两阶段增强方法：先通过HSV色彩空间分解实现亮度校正，再仅对明度（V）通道应用限制对比度自适应直方图均衡化（CLAHE）。在公开的DRIVE数据集（40张图像，584×565像素，Canon CR5相机，眼科医生标注真值）上验证，采用峰值信噪比（PSNR）、结构相似性指数（SSIM）和信噪比（CNR）进行定量评估。对比标准直方图均衡化（HE）与自适应直方图均衡化（AHE），所提方法在所有指标上均表现更优：PSNR达29.3 dB，SSIM为0.91，CNR为3.12；平均处理时间仅0.14秒/图。后续通过二值掩码提取疑似血管病变的高反射区域。结论表明，该方法显著提升图像对比度与结构保真度，且处理速度满足临床筛查需求。
原文摘要 · Abstract (English)
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.
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