4.3 Article

Adaptively determining regularisation parameters in non-local total variation regularisation for image denoising

期刊

ELECTRONICS LETTERS
卷 51, 期 2, 页码 144-U26

出版社

INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/el.2014.3494

关键词

image denoising; tensors; nonlocal selection scheme; nonlocal total variation regularisation; image denoising; NLTV regularisation; content-aware function; image contents; nonlocal structure tensor; NLST; simulated images; real noisy images; visual improvement; peak signal-to-noise ratio value; PSNR value

资金

  1. NSFC [61402235, 61173072]
  2. Natural Science Foundation of Jiangsu Province [BK2011825]
  3. NSFC-NRF Cooperation Program [61311140264]
  4. National Research Foundation of Korea [NRF-2013K2A2A2000777]
  5. Jiangsu Engineering Centre of Network Monitoring [KJR1105]
  6. PAPD

向作者/读者索取更多资源

A non-local selection scheme of regularisation parameters is exploited for non-local total variation (NLTV) regularisation, based on a content-aware function that can determine regularisation parameters through discriminating image contents, with the aid of a non-local structure tensor calculated using the weights from the original NLTV. Experimental results on simulated and real noisy images show the relatively good performance of the proposed scheme based on NLTV, in terms of visual improvement and peak signal-to-noise ratio value.

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