4.3 Article

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

Journal

ELECTRONICS LETTERS
Volume 51, Issue 2, Pages 144-U26

Publisher

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

Keywords

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

Funding

  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

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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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