Luminosity and contrast enhancement of retinal vessel images using weighted average histogram

Abstract

Retinal imaging can detect a variety of common eye illnesses as well as cardiovascular diseases. At the same time, poor-quality retinal images are impossible to diagnose due to non-uniform illumination, image obscuration, and low contrast. A novel enhancement method is proposed to improve the luminosity and contrast of the color retinal image. This algorithm is applied to the publicly available structured analysis of the retina (STARE) image dataset, and promising results are obtained. Contrast enhancement and luminosity enhancement are the two sections of the proposed method. Gamma adjustment on the V channel in hue, saturation and value (HSV) color model gives a luminosity gain matrix, which improves the image luminosity. Weighted average histogram equalization (WAHE) acts on the luminosity channel (L) of the L*a*b* (luminosity and chromaticity) model for contrast improvement. Finally, an effective luminosity and contrast adjustment scheme is presented for various retinal vessel images, and its performance is illustrated against the different state-of-the-art methods using a well-known numerical metric.