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2018 | OriginalPaper | Chapter

Expected Patch Log Likelihood Based on Multi-layer Prior Information Learning

Authors : ShunFeng Wang, JiaCen Xie, YuHui Zheng, Tao Jiang, ShuHang Xue

Published in: Advances in Computer Science and Ubiquitous Computing

Publisher: Springer Singapore

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Abstract

How to preserve the edge and texture details has been a difficult problem in image denoising. In this paper, we propose a multi-layer prior information learning method, which combines the statistical and geometric features of the image to describe the attributes of the prior information more accurately and completely. The experimental results show that our proposed method is superior to the EPLL (Expected patch log likelihood) method with a single statistical characteristic for a priori learning in both visual and quantitative evaluation.

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Metadata
Title
Expected Patch Log Likelihood Based on Multi-layer Prior Information Learning
Authors
ShunFeng Wang
JiaCen Xie
YuHui Zheng
Tao Jiang
ShuHang Xue
Copyright Year
2018
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-10-7605-3_49