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An Expected Patch Log Likelihood Denoising Method Based on Internal and External Image Similarity
School of Math and Statistics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
* Corresponding Author: Jianwei Zhang. Email: .
Journal on Internet of Things 2020, 2(1), 13-21. https://doi.org/10.32604/jiot.2020.09073
Received 08 November 2019; Accepted 27 November 2019; Issue published 06 August 2020
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Nonlocal property is an important feature of natural images, which means that the patch matrix formed by similar image patches is low-rank. Meanwhile, learning good image priors is of great importance for image denoising. In this paper, we combine the image self-similarity with EPLL (Expected patch log likelihood) method, and propose an EPLL denoising model based on internal and external image similarity to improve the preservation of image details. The experiment results show that the validity of our method is proved from two aspects of visual and numerical results.Keywords
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APA Style
Xu, P., Zhang, J. (2020). An expected patch log likelihood denoising method based on internal and external image similarity. Journal on Internet of Things, 2(1), 13-21. https://doi.org/10.32604/jiot.2020.09073
Vancouver Style
Xu P, Zhang J. An expected patch log likelihood denoising method based on internal and external image similarity. J Internet Things . 2020;2(1):13-21 https://doi.org/10.32604/jiot.2020.09073
IEEE Style
P. Xu and J. Zhang, “An Expected Patch Log Likelihood Denoising Method Based on Internal and External Image Similarity,” J. Internet Things , vol. 2, no. 1, pp. 13-21, 2020. https://doi.org/10.32604/jiot.2020.09073
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Copyright © 2020 The Author(s). Published by Tech Science Press.This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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