Enhancing low-light images using Sakaguchi type function and Gegenbauer polynomial

Enhancing low-light images is crucial for various applications in computer vision, yet current approaches often fall short in balancing image quality and detail preservation. This study introduces a novel method designed to enhance low-light images by applying advanced mathematical techniques from geometric function theory. Specifically, we employ Sakaguchi-type class functions, subordinated with the Gegenbeur polynomial, to derive coefficient estimations. These estimations are then used in convolution kernels to produce enhanced image versions. The method was tested on the LOw-Light dataset (LOL), containing challenging low-light images with noise and artifacts. Our approach's effectiveness is validated through quantitative metrics, including PSNR and SSIM, as well as visual comparisons. The results demonstrate significant improvements over existing state-of-the-art methods, offering better visibility and detail retention. This method holds promise for enhancing images in critical fields such as surveillance and medical imaging.