A Structure-Aware and Color-Preserving Retinal Fundus Image Enhancement Framework for Improved Clinical Diagnosis and Automated Analysis

Enhancing retinal fundus images is an indispensable technique in ophthalmology for the detection, diagnosis and treatment textbook of eye diseases. The problem with most enhancement methods is that they tend to distort colors, produce artifacts of over-enhancement, are not very robust when applied to other datasets, and are rarely validated for downstream clinical applications. This paper presents a structure-aware and color-preserving retinal image enhancement setup that is aimed at not only improving the contrast and visibility of retinal structures but also at preserving diagnostic fidelity. 

The main feature of the method is the combination and orchestration of adaptive illumination correction, selective contrast enhancement, retinal structure preservation and perceptually guided color restoration steps into a consistent and cohesive enhancement pipeline. The proposed system uses luminance-chrominance separation to control illumination and color consistency independently. 

Besides, retinal vessel segmentation and diabetic retinopathy classification tasks are used for downstream validation to highlight clinical relevance. Testing on various public retinal datasets has shown that the method proposed here can elevate image quality, preserve perceptual consistency and enhance diagnostic visibility while its low computational load makes it suitable for tele-ophthalmology and real-time screening applications.