IEEE Trans Image Process - An iterative L1-based image restoration algorithm with an adaptive parameter estimation.

Tópicos

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Resumo

Regularization methods for the solution of ill-posed inverse problems can be successfully applied if a right estimation of the regularization parameter is known. In this paper, we consider the L(1)-regularized image deblurring problem and evaluate its solution using the iterative forward-backward splitting method. Based on this approach, we propose a new adaptive rule for the estimation of the regularization parameter that, at each iteration, dynamically updates the parameter value, following the evolution of the objective functional. The iterative algorithm automatically stops, without requiring any assumption about the perturbation process, when the parameter has reached a seemingly near optimal value. In spite of the fact that the optimality of this value has not yet been theoretically proved, a large number of numerical experiments confirm that the proposed rule yields restoration results competitive with those of the best state-of-the-art algorithms.

Resumo Limpo

regular method solut illpos invers problem can success appli right estim regular paramet known paper consid lregular imag deblur problem evalu solut use iter forwardbackward split method base approach propos new adapt rule estim regular paramet iter dynam updat paramet valu follow evolut object function iter algorithm automat stop without requir assumpt perturb process paramet reach seem near optim valu spite fact optim valu yet theoret prove larg number numer experi confirm propos rule yield restor result competit best stateoftheart algorithm

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