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High-quality image restoration from partial random samples in spatial domain

In this paper, a novel algorithm for high-quality image restoration is proposed. The contributions of this work are two-fold. First, a new form of minimization function for solving image inverse problems is formulated via combining local total variation model and nonlocal adaptive 3-D sparse representation model as regularizers under the regularization-based framework. Second, a new Split-Bregman based iterative algorithm is developed to solve the above optimization problem efficiently associated with proved theoretical convergence property. Experimental results on image restoration from partial random samples have shown that the proposed algorithm achieves significant performance improvements over the current state-of-the-art schemes and exhibits nice convergence property.