IEEE Trans Image Process - Parameter estimation for blind and non-blind deblurring using residual whiteness measures.

Tópicos

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Resumo

Image deblurring (ID) is an ill-posed problem typically addressed by using regularization, or prior knowledge, on the unknown image (and also on the blur operator, in the blind case). ID is often formulated as an optimization problem, where the objective function includes a data term encouraging the estimated image (and blur, in blind ID) to explain the observed data well (typically, the squared norm of a residual) plus a regularizer that penalizes solutions deemed undesirable. The performance of this approach depends critically (among other things) on the relative weight of the regularizer (the regularization parameter) and on the number of iterations of the algorithm used to address the optimization problem. In this paper, we propose new criteria for adjusting the regularization parameter and/or the number of iterations of ID algorithms. The rationale is that if the recovered image (and blur, in blind ID) is well estimated, the residual image is spectrally white; contrarily, a poorly deblurred image typically exhibits structured artifacts (e.g., ringing, oversmoothness), yielding residuals that are not spectrally white. The proposed criterion is particularly well suited to a recent blind ID algorithm that uses continuation, i.e., slowly decreases the regularization parameter along the iterations; in this case, choosing this parameter and deciding when to stop are one and the same thing. Our experiments show that the proposed whiteness-based criteria yield improvements in SNR, on average, only 0.15 dB below those obtained by (clairvoyantly) stopping the algorithm at the best SNR. We also illustrate the proposed criteria on non-blind ID, reporting results that are competitive with state-of-the-art criteria (such as Monte Carlo-based GSURE and projected SURE), which, however, are not applicable for blind ID.

Resumo Limpo

imag deblur id illpos problem typic address use regular prior knowledg unknown imag also blur oper blind case id often formul optim problem object function includ data term encourag estim imag blur blind id explain observ data well typic squar norm residu plus regular penal solut deem undesir perform approach depend critic among thing relat weight regular regular paramet number iter algorithm use address optim problem paper propos new criteria adjust regular paramet andor number iter id algorithm rational recov imag blur blind id well estim residu imag spectral white contrarili poor deblur imag typic exhibit structur artifact eg ring oversmooth yield residu spectral white propos criterion particular well suit recent blind id algorithm use continu ie slowli decreas regular paramet along iter case choos paramet decid stop one thing experi show propos whitenessbas criteria yield improv snr averag db obtain clairvoy stop algorithm best snr also illustr propos criteria nonblind id report result competit stateoftheart criteria mont carlobas gsure project sure howev applic blind id

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