A Variational Model for Mixed Noise Removal Using Weberized Total Variation Regularization
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Abstract
The problem of removing noise from digital images has attracted considerable attention as it can improve image quality and the visual interpretability of information. In the case of medical imaging, effective denoising can lead to more accurate diagnoses and reduce the likelihood of errors. In this research, the researchers developed the ASN-WTV model to eliminate mixed additive and speckle noise by using the data fitting term of the ROF model to eliminate additive noise, the data fitting term of the KKWV model to eliminate speckle noise, and the Weberized total variation regularization. Two numerical methods, namely the fixed point method and the split Bregman method, were presented to find the solution of the ASN-WTV model and compare the performance of the resulting images with that of the KKWV-TVL model. The numerical experimental results showed that the ASN-WTV model provided better quality output images than the KKWV-TVL model and that the split Bregman method performed better than the fixed point method.
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References
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