Retinal image enhancement using adaptive histogram equalization tuned with nonsimilar grouping curvelet

Fundus images are broadly used by medical ophthalmologists to detect and assess any customary abnormalities. Fundus imaging sensors capture the eye's rigid portion, which characteristically covers the core, tangential retina, optic disc, and macula. Existing state-of-the-art fundus sensors have the drawback of producing low contrast and noisy information, which makes scientific and algorithmic evaluation very complicated. This article proposes an Adaptive Histogram Equalization-Tuned with Nonsimilar Grouping Curvelet (HET-NOSCU), which works through a joint denoising enhancement approach. The proposed algorithm's main contribution includes (i) use of curvelet features to better preserve edges during denoising. (ii) Adaptive enhancement using the histogram to prevent halo ringing and specular artifacts, which yields superior results than the very recently established state-of -the-art methods, using similar performing parameters such as peak signal to noise ratio (PSNR), structural similarity index (SSIM), and correlation coefficient (CoC). We observe an improvement of 17.66%, 0.93%, and 0.24%, respectively, for the above parameters.