Abstract
For the pursuit of high-quality restoration effects, we introduce a novel non-convex extended scheme to reconstruct blurred images under impulse noise in this paper. Because of the remarkable advantages of the combination of non-convex ℓp-norm and fractional-order total variation regularization, our developed strategy possesses a brilliant ability to overcome the staircase-like aspects and preserve neat contours. To obtain the minimizer of the optimization problem, we adopt the alternating direction method of multipliers closely integrating with the iteratively reweighted ℓ1 algorithm. Theoretically, a convergence proof is provided for the developed algorithm. By considering salt-and-pepper noise and random-valued impulse noise, comprehensive simulation comparisons with some popular methods are carried out for image restoration. And the experimental results illustrate that our proposed model behaves better with respect to visual comparison and quantitative measurement.
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