IEEE Trans Image Process - A fast adaptive parameter estimation for total variation image restoration.

Tópicos

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Resumo

Estimation of the regularization parameter, which strikes a balance between the data fidelity and regularity, is essential for successfully solving ill-posed image restoration problems. Based on the classical total variation (TV) model and prevalent alternating direction method of multipliers, we hammer out a fast algorithm being able to simultaneously estimate the regularization parameter and restore the degraded image. By applying variable splitting technique to both the regularization term and data fidelity term, we overcome the nondifferentiability of TV and achieve a closed form to update the regularization parameter in each iteration. The solution is guaranteed to satisfy Morozov's discrepancy principle. Furthermore, we present a convergence proof for the proposed algorithm on the premise of a variable regularization parameter. Experimental results demonstrate that the proposed algorithm is superior in speed and competitive in accuracy compared with several state-of-the-art methods. Besides, the proposed method can be smoothly extended to the multichannel image restoration.

Resumo Limpo

estim regular paramet strike balanc data fidel regular essenti success solv illpos imag restor problem base classic total variat tv model preval altern direct method multipli hammer fast algorithm abl simultan estim regular paramet restor degrad imag appli variabl split techniqu regular term data fidel term overcom nondifferenti tv achiev close form updat regular paramet iter solut guarante satisfi morozov discrep principl furthermor present converg proof propos algorithm premis variabl regular paramet experiment result demonstr propos algorithm superior speed competit accuraci compar sever stateoftheart method besid propos method can smooth extend multichannel imag restor

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