In this paper we present an iterative algorithm for the solution of regularization problems arising in inverse image processing. The regularization function to be minimized is constituted by two terms, a data fit function and a regularization function, weighted by a regularization parameter. The proposed algorithm solves the minimization problem and estimates the regularization parameter by an iterative procedure. Numerical results on image denoising, deblurring and tomographic images reconstruction show that the method is efficient and computationally fast in these applications.
View An algorithm for the choice of the regularization parameter in inverse problems in imaging