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Symmetry 2018, 10(5), 167; https://doi.org/10.3390/sym10050167

Image Denoising via Improved Dictionary Learning with Global Structure and Local Similarity Preservations

1
School of Automation, Guangdong University of Technology, Guangzhou 510006, China
2
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
3
Department of Computer Science, Southern Illinois University-Carbondale, Carbondale, IL 62901, USA
4
College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
5
Department of Mathematics, Southern Illinois University-Carbondale, Carbondale, IL 62901, USA
*
Author to whom correspondence should be addressed.
Received: 4 April 2018 / Revised: 14 May 2018 / Accepted: 14 May 2018 / Published: 16 May 2018
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Abstract

We proposed a new efficient image denoising scheme, which leads to four important contributions. The first is to integrate both reconstruction and learning based approaches into a single model so that we are able to benefit advantages from both approaches simultaneously. The second is to handle both multiplicative and additive noise removal problems. The third is that the proposed approach introduces a sparse term to reduce non-Gaussian outliers from multiplicative noise and uses a Laplacian Schatten norm to capture the global structure information. In addition, the image is represented by preserving the intrinsic local similarity via a sparse coding method, which allows our model to incorporate both global and local information from the image. Finally, we propose a new method that combines Method of Optimal Directions (MOD) with Approximate K-SVD (AK-SVD) for dictionary learning. Extensive experimental results show that the proposed scheme is competitive against some of the state-of-the-art denoising algorithms. View Full-Text
Keywords: image denoising; novel dictionary; learning algorithms image denoising; novel dictionary; learning algorithms
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Cai, S.; Kang, Z.; Yang, M.; Xiong, X.; Peng, C.; Xiao, M. Image Denoising via Improved Dictionary Learning with Global Structure and Local Similarity Preservations. Symmetry 2018, 10, 167.

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