Accelerated Graph-based Nonlinear Denoising Filters

Denoising filters, such as bilateral, guided, and total variation filters, applied to images on general graphs may require repeated application if noise is not small enough. We formulate two acceleration techniques of the resulted iterations: conjugate gradient method and Nesterov's acceleration. We numerically show efficiency of the accelerated nonlinear filters for image denoising and demonstrate 2-12 times speed-up, i.e., the acceleration techniques reduce the number of iterations required to reach a given peak signal-to-noise ratio (PSNR) by the above indicated factor of 2-12.

• Publication year

  2015

• Venue

  International Conference on Conceptual Structures

• Publication date

  2015-12-01

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1016/j.procs.2016.05.348arXiv 1512.00389
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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