IEEE Trans Image Process - A weighted dictionary learning model for denoising images corrupted by mixed noise.

Tópicos

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Resumo

This paper proposes a general weighted l(2)-l(0) norms energy minimization model to remove mixed noise such as Gaussian-Gaussian mixture, impulse noise, and Gaussian-impulse noise from the images. The approach is built upon maximum likelihood estimation framework and sparse representations over a trained dictionary. Rather than optimizing the likelihood functional derived from a mixture distribution, we present a new weighting data fidelity function, which has the same minimizer as the original likelihood functional but is much easier to optimize. The weighting function in the model can be determined by the algorithm itself, and it plays a role of noise detection in terms of the different estimated noise parameters. By incorporating the sparse regularization of small image patches, the proposed method can efficiently remove a variety of mixed or single noise while preserving the image textures well. In addition, a modified K-SVD algorithm is designed to address the weighted rank-one approximation. The experimental results demonstrate its better performance compared with some existing methods.

Resumo Limpo

paper propos general weight ll norm energi minim model remov mix nois gaussiangaussian mixtur impuls nois gaussianimpuls nois imag approach built upon maximum likelihood estim framework spars represent train dictionari rather optim likelihood function deriv mixtur distribut present new weight data fidel function minim origin likelihood function much easier optim weight function model can determin algorithm play role nois detect term differ estim nois paramet incorpor spars regular small imag patch propos method can effici remov varieti mix singl nois preserv imag textur well addit modifi ksvd algorithm design address weight rankon approxim experiment result demonstr better perform compar exist method

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