Abstract
Although the expected patch log likelihood (EPLL) achieves good performance for denoising, an inherent nonadaptive problem exists. To solve this problem, an adaptive learning is introduced into the EPLL in this paper. Inspired from the structured sparse dictionary, an adaptive Gaussian mixture model (GMM) is proposed based on patch priors. The maximum a posteriori estimation is employed to cluster and update the image patches. Also, the new image patches are used to update the GMM. We perform these two steps alternately until the desired denoised results are achieved. Experimental results show that the proposed denoising method outperforms the existing image denoising algorithms.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61001179, 61372173, and 61201393), the Natural Science Foundation of Guangdong Province, China (No. S2011040004079), and the Project on Integration of Production, Education and Research, Guangdong Province, and Ministry of Education, China (No. 2012B091100424).
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Cai, N., Zhou, Y., Wang, S. et al. Image denoising via patch-based adaptive Gaussian mixture prior method. SIViP 10, 993–999 (2016). https://doi.org/10.1007/s11760-015-0850-9
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DOI: https://doi.org/10.1007/s11760-015-0850-9