Fast algorithm for box‐constrained fractional‐order total variation image restoration with impulse noise
Jianguang Zhu, Juan Wei, Binbin Hao · IET Image Processing · 2022
Abstract In this paper, a novel variational model with box constraints is proposed to restore images corrupted by impulse noise. The proposed model is composed of fractional‐order total variation regularization and L p ‐fidelity term . Moreover, the new model possesses the advantages of preserving sharp edges and removing blocking effect. To solve the proposed model, some auxiliary variables are first introduced to transform it into some easy‐to‐solve subproblems. Further, the alternating direction method of multipliers, iteratively re‐weighted ℓ 1 algorithm and fast iteration technique are adopted to solve the related subproblems. Numerical results show that the proposed model performs better in comparison with the several existing methods, in terms of both quantitative evaluation and visual quality.