IEEE Trans Image Process - Robust multichannel blind deconvolution via fast alternating minimization.

Tópicos

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Resumo

Blind deconvolution, which comprises simultaneous blur and image estimations, is a strongly ill-posed problem. It is by now well known that if multiple images of the same scene are acquired, this multichannel (MC) blind deconvolution problem is better posed and allows blur estimation directly from the degraded images. We improve the MC idea by adding robustness to noise and stability in the case of large blurs or if the blur size is vastly overestimated. We formulate blind deconvolution as an l(1) -regularized optimization problem and seek a solution by alternately optimizing with respect to the image and with respect to blurs. Each optimization step is converted to a constrained problem by variable splitting and then is addressed with an augmented Lagrangian method, which permits simple and fast implementation in the Fourier domain. The rapid convergence of the proposed method is illustrated on synthetically blurred data. Applicability is also demonstrated on the deconvolution of real photos taken by a digital camera.

Resumo Limpo

blind deconvolut compris simultan blur imag estim strong illpos problem now well known multipl imag scene acquir multichannel mc blind deconvolut problem better pose allow blur estim direct degrad imag improv mc idea ad robust nois stabil case larg blur blur size vast overestim formul blind deconvolut l regular optim problem seek solut altern optim respect imag respect blur optim step convert constrain problem variabl split address augment lagrangian method permit simpl fast implement fourier domain rapid converg propos method illustr synthet blur data applic also demonstr deconvolut real photo taken digit camera

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