When physics meets signal processing: Image and video denoising based on ising theory

Eliahu Cohen, Ron Heiman, Maya Carmi, Ofer Hadar, Asaf Cohen

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Abstract In this work we suggest a novel model for automatic noise estimation and image denoising. In particular, we investigate the useful affinity which arises between statistical mechanics and image processing, and describe a framework from which novel denoising algorithms can be derived: Ising-like models and simulated annealing techniques. This is the first time such algorithms are used for colored images and video denoising. Results, as well as benchmarks, suggest a significant gain in PSNR and SSIM in comparison to other filters, mainly in cases of low impulse noise. When hybridizing our models with other image processing techniques they are shown to be even more effective. Their major disadvantages- high complexity and limited applicability, are also discussed.

Original languageEnglish
Article number14935
Pages (from-to)14-21
Number of pages8
JournalSignal Processing: Image Communication
StatePublished - 1 May 2015
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2015 Elsevier B.V. All rights reserved.


  • Image denoising
  • Ising model
  • Metropolis algorithm
  • Monte-Carlo methods
  • Simulated annealing
  • Statistical physics


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