Generalized Caputo–Fabrizio fractional operator: an application in image denoising

The aim of the present paper is to propose the algorithm using the Caputo–Fabrizio fractional integral operator of non-singular type with the Mittag-Leffler function in the generalized form to find the coefficients of a kernel to remove the noise from images. The performance of the proposed algorithm is compared through the different numerical parameters such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structure Similarity Index measure (SSIM) and Image Enhancement Factor (IEF). The analysis of the proposed algorithm is also investigated through visual perception with the other prevailed algorithms viz. Median Filter (MF), Riemann-Liouville filter (RLF), Improved Grunwald Letnikov filter (IGLF), Alexander fractional differential filter (AFDF), Alexander fractional integral filter (AFIF), Algorithm based on small probability strategy (AFC-SPS), Algorithm based on entropy and gradient feature (ENA-FC). Simulation results demonstrate that the proposed technique based on generalized fractional operators is much improved than other existing algorithms.