Limited memory methods with improved symmetric rank-one updates and its applications on nonlinear image restoration

The iterative solution of unconstrained optimization problems has been found in a variety of significant applications of research areas, such as image restoration. In this paper, we present an efficient limited memory quasi-Newton technique based on symmetric rank-one updating formula to compute meaningful solutions for large-scale problems arising in some image restoration problems. Numerical experiments and comparisons on various well-known methods in the literature are presented to illustrate the effectiveness of the proposed method particularly for images of large size.

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