Luminosity-Adaptive Contrast Enhancement Using CLAHE for Retinal Fundus Images with Quantitative Validation and Comparative Analysis
2026-07-20 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionEmerging Technologies
AI summaryⓘ
The authors developed a new way to make retinal images clearer for doctors to spot eye diseases. They combined two techniques: adjusting brightness in specific color channels and improving contrast using a special method called CLAHE. When tested on a known dataset, their method produced sharper, clearer images faster than older methods. This can help improve eye disease screening, though they suggest exploring advanced AI methods next.
Retinal fundus imagingDiabetic retinopathyLuminosity correctionHSV color spaceContrast Limited Adaptive Histogram Equalization (CLAHE)Peak Signal-to-Noise Ratio (PSNR)Structural Similarity Index (SSIM)Contrast-to-Noise Ratio (CNR)Histogram Equalization (HE)Image enhancement
Authors
K. Mithra, Prem Kumar Santhanam
Abstract
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.