East Asian J. Appl. Math., 8 (2018), pp. 586-597.
Published online: 2018-08
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A variational $ℓ_q$-seminorm model to reduce the impulse noise is proposed. For $0<q<1$, it captures sparsity better than the $ℓ_1$-norm model. Numerical experiments show that for small $q$ this model is more efficient than TV$ℓ_1$ model if the noise level is low. If the noise level grows, the best possible parameter $q$ in the model approaches 1.
}, issn = {2079-7370}, doi = {https://doi.org/10.4208/eajam.101117.130418}, url = {http://global-sci.org/intro/article_detail/eajam/12627.html} }A variational $ℓ_q$-seminorm model to reduce the impulse noise is proposed. For $0<q<1$, it captures sparsity better than the $ℓ_1$-norm model. Numerical experiments show that for small $q$ this model is more efficient than TV$ℓ_1$ model if the noise level is low. If the noise level grows, the best possible parameter $q$ in the model approaches 1.