Abstract
Regularization parameter selection for image denoising has always been a hot issue. In this paper, an adaptive regularization parameter selection method is exploited for the Gaussian Mixture Model (GMM) based image restoration by combining the gradient matching and the local entropy of the image, which varies with different regions of the image and has a good robustness to noise. Experiment results demonstrate that our proposed adaptive regularization parameter for GMM based image restoration method performs comparatively well, both in visual effects and quantitative evaluations.
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This work was supported in part by the NSFC (Grants 61402234 and 61402235) and the PAPD.
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Zhang, J.W., Liu, J., Zheng, Y.H., Wang, J. (2017). Regularization Parameter Selection for Gaussian Mixture Model Based Image Denoising Method. In: Park, J., Pan, Y., Yi, G., Loia, V. (eds) Advances in Computer Science and Ubiquitous Computing. UCAWSN CUTE CSA 2016 2016 2016. Lecture Notes in Electrical Engineering, vol 421. Springer, Singapore. https://doi.org/10.1007/978-981-10-3023-9_47
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DOI: https://doi.org/10.1007/978-981-10-3023-9_47
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