IEEE Trans Image Process - Automatic parameter selection for denoising algorithms using a no-reference measure of image content.

Tópicos

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Resumo

Across the field of inverse problems in image and video processing, nearly all algorithms have various parameters which need to be set in order to yield good results. In practice, usually the choice of such parameters is made empirically with trial and error if no "ground-truth" reference is available. Some analytical methods such as cross-validation and Stein's unbiased risk estimate (SURE) have been successfully used to set such parameters. However, these methods tend to be strongly reliant on restrictive assumptions on the noise, and also computationally heavy. In this paper, we propose a no-reference metric Q which is based upon singular value decomposition of local image gradient matrix, and provides a quantitative measure of true image content (i.e., sharpness and contrast as manifested in visually salient geometric features such as edges,) in the presence of noise and other disturbances. This measure 1) is easy to compute, 2) reacts reasonably to both blur and random noise, and 3) works well even when the noise is not Gaussian. The proposed measure is used to automatically and effectively set the parameters of two leading image denoising algorithms. Ample simulated and real data experiments support our claims. Furthermore, tests using the TID2008 database show that this measure correlates well with subjective quality evaluations for both blur and noise distortions.

Resumo Limpo

across field invers problem imag video process near algorithm various paramet need set order yield good result practic usual choic paramet made empir trial error groundtruth refer avail analyt method crossvalid stein unbias risk estim sure success use set paramet howev method tend strong reliant restrict assumpt nois also comput heavi paper propos norefer metric q base upon singular valu decomposit local imag gradient matrix provid quantit measur true imag content ie sharp contrast manifest visual salient geometr featur edg presenc nois disturb measur easi comput react reason blur random nois work well even nois gaussian propos measur use automat effect set paramet two lead imag denois algorithm ampl simul real data experi support claim furthermor test use tid databas show measur correl well subject qualiti evalu blur nois distort

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