A combined first and fractional order regularization method for mixed Poisson-white spike noisy image restoration

In this paper we study the problem of restoring images affected by mixed Poisson-Square Cauchy noise. A multi-convex variational model is derived integrating infimal convolution likelihood into a process joint maximum posteriori estimation, where modified four-directional fractional-order total variation united with first order to characterize image prior. To solve proposed numerically, block coefficient descent based algorithm derived, in which variable splitting and alternating direction minimization multipliers are utilized along anisotropic diffusion additive operator gain efficiency quality. The obtained numerical results compared from two other regularized models different fidelities. discuss confirms flexibility validity model.