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Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 4/2018

05.04.2017 | Theoretical Advances

Image denoising with norm weighted fusion estimators

verfasst von: Nazeer Muhammad, Nargis Bibi, Adnan Jahangir, Zahid Mahmood

Erschienen in: Pattern Analysis and Applications | Ausgabe 4/2018

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Abstract

In recent era, the weighted matrix rank minimization is used to reduce image noise, promisingly. However, low-rank weighted conditions may cause oversmoothing or oversharpening of the denoised image. This demands a clever engineering algorithm. Particularly, to remove heavy noise in image is always a challenging task, specially, when there is need to preserve the fine edge structures. To attain a reliable estimate of heavy noise image, a norm weighted fusion estimators method is proposed in wavelet domain. This holds the significant geometric structure of the given noisy image during the denoising process. Proposed method is applied on standard benchmark images, and simulation results outperform the most popular rivals of noise reduction approaches, such as BM3D, EPLL, LSSC, NCSR, SAIST, and WNNM in terms of the quality measurement metric PSNR (dB) and structural analysis SSIM indices.
Vorheriger Artikel Digital watermarking using Hall property image decomposition method
Nächster Artikel A fast classification strategy for SVM on the large-scale high-dimensional datasets

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Metadaten
Titel
Image denoising with norm weighted fusion estimators
verfasst von
Nazeer Muhammad
Nargis Bibi
Adnan Jahangir
Zahid Mahmood
Publikationsdatum
05.04.2017
Verlag
Springer London
Erschienen in
Pattern Analysis and Applications / Ausgabe 4/2018
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-017-0617-8

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