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An efficient image denoising method based on principal component analysis with learned patch groups

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Abstract

We propose a novel principal component analysis (PCA)-based image denoising framework motivated by the fact that the PCA along with patch groups (PGs) can produce better denoising performance. The PGs essentially capture the geometric information from noisy image. In the denoising stage, the learned PGs are transformed to PCA domain and it only preserves the important principal components while removing the noise components. The proposed denoising method consists of mainly three stages such as patch grouping stage, dictionary learning stage and PCA-based denoising stage. Also, we present an algorithm Learned-PGPCA and tested it in a simulated environment. The experimental results divulged that the proposed denoising framework Learned-PGPCA achieved very competitive denoising performance, particularly in preserving edges and textures as compared to recent patch-based image denoising methods pertaining to gaussian noise.

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Authors and Affiliations

  1. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China

    Sidheswar Routray

  2. School of Electronics Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India

    Arun Kumar Ray

  3. Department of Instrumentation and Electronics, College of Engineering and Technology (CET), Bhubaneswar, Odisha, India

    Chandrabhanu Mishra

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  1. Sidheswar Routray
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  2. Arun Kumar Ray
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  3. Chandrabhanu Mishra
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Correspondence to Sidheswar Routray.

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Routray, S., Ray, A.K. & Mishra, C. An efficient image denoising method based on principal component analysis with learned patch groups. SIViP 13, 1405–1412 (2019). https://doi.org/10.1007/s11760-019-01489-2

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  • DOI: https://doi.org/10.1007/s11760-019-01489-2

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