A hybrid-optimization method for large-scale non-negative full regularization in image restoration

作者：Guerrero Johana^*; Raydan Marcos; Rojas Marielba

来源：Inverse Problems in Science and Engineering, 2013, 21(5): 741-766.

DOI：10.1080/17415977.2012.720684

• image restoration
• ill-posed problems
• regularization
• spectral projected gradient
• simulated annealing

We describe a new hybrid-optimization method for solving the full-regularization problem of computing both the regularization parameter and the corresponding regularized solution in 1-norm and 2-norm Tikhonov regularizations with additional non-negativity constraints. The approach combines the simulated annealing technique for global optimization and the low-cost spectral projected gradient method for the minimization of large-scale smooth functions on convex sets. The new method is matrix-free in the sense that it relies on matrix-vector multiplications only. We describe the method and discuss some of its properties. Numerical results indicate that the new approach is a promising tool for solving large-scale image restoration problems.

• 出版日期2013-7-1