Abstract
Gaussian mixture model is commonly used as image prior model to solve image restoration problem. However, vector representation leads to lose the inherent spatial relevant information and cause unstable estimation. In this paper, a mixture of matrix normal distributions (MMND) based image restoration algorithm is proposed, which incorporates the hidden structural information into prior image modeling. MMND is used as the prior image model and expectation maximization algorithm is used to optimize the maximum posterior criterion. Experiments conducted on color images indicate that MMND can achieve better peak signal to noise ratio (PSNR) as compared to other state-of-the-art methods.
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Acknowledgment
This work is fully supported by the grants from Beijing Natural Science Foundation (Project No. 4162027), the National Natural Science Foundation of China (61375045) and the Joint Research Fund in Astronomy (U1531242) under cooperative agreement between the National Natural Science Foundation of China (NSFC) and Chinese Academy of Sciences (CAS). Prof. Ping Guo and Xiuling Zhou are the authors to whom all the correspondence should be addressed.
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Zhou, X., Wang, J., Guo, P., Chen, C.L.P. (2017). Mixture of Matrix Normal Distributions for Color Image Inpainting. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10636. Springer, Cham. https://doi.org/10.1007/978-3-319-70090-8_10
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DOI: https://doi.org/10.1007/978-3-319-70090-8_10
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