Abstract
Recovering images corrupted by additive white Gaussian noise (AWGN) and impulse noise (IN) is a challenging difficulty. This paper for the first time proposes the definition of the higher-order Atangana-Baleanu (AB) fractional calculus and combines it with the simultaneous non-local self-similarity (SNSS) model organically to obtain the ABSNSS model for image denoising. The model can effectively restore images damaged by AWGN and IN, while preserving the texture and edge features of the images, and has strong denoising ability. Furthermore, the convergence of the iterative format of the model is demonstrated theoretically. Finally, numerical experiments show that the proposed ABSNSS model has significant advantages in removing the mixed noise and performs better than the state-of-the-art methods.
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