Wavelet-based retinal image enhancement

Abstract

Retinal images provide a simple non-invasive method for the detection of several eye diseases. However, many factors can result in the degradation of the images’ quality, thus affecting the reliability of the performed diagnosis. Enhancement of retinal images is thus essential to increase the overall image quality. In this work, a wavelet-based retinal image enhancement algorithm is proposed that considers four different common quality issues within retinal images (1) noise removal, (2) sharpening, (3) contrast enhancement and (4) illumination enhancement. Noise removal and sharpening are performed by processing the wavelet detail subbands, such that the upper detail coefficients are eliminated, whereas bilinear mapping is used to enhance the lower detail coefficients based on their relevance. Contrast and illumination enhancement involve applying contrast limited adaptive histogram equalization (CLAHE) and the proposed luminance boosting method to the approximation subband, respectively. Four different retinal image quality measures are computed to assess the proposed algorithm and to compare its performance against four other methods from literature. The comparison showed that the introduced method resulted in the highest overall image improvement followed by spatial CLAHE for all the considered quality measures; thus, indicating the superiority of the proposed wavelet-based enhancement method.