Sparsity-based poisson denoising with dictionary learning

Raja Giryes*, Michael Elad

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The problem of Poisson denoising appears in various imaging applications, such as low-light photography, medical imaging, and microscopy. In cases of high SNR, several transformations exist so as to convert the Poisson noise into an additive-independent identically distributed. Gaussian noise, for which many effective algorithms are available. However, in a low-SNR regime, these transformations are significantly less accurate, and a strategy that relies directly on the true noise statistics is required. Salmon et al. took this route, proposing a patch-based exponential image representation model based on Gaussian mixture model, leading to state-of-the-art results. In this paper, we propose to harness sparse-representation modeling to the image patches, adopting the same exponential idea. Our scheme uses a greedy pursuit with boot-strapping-based stopping condition and dictionary learning within the denoising process. The reconstruction performance of the proposed scheme is competitive with leading methods in high SNR and achieving state-of-the-art results in cases of low SNR.

Original languageEnglish
Article number6918528
Pages (from-to)5057-5069
Number of pages13
JournalIEEE Transactions on Image Processing
Issue number12
StatePublished - 1 Dec 2014
Externally publishedYes


  • Denoising
  • Poisson noise
  • dictionary learning
  • photon-limited imaging
  • signal modeling
  • sparse representations


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