Hauptmenü
  • Autor
    • Chen, Yunjin
    • Feng, Wensen
    • Ranftl, Rene
    • Qiao, Hong
    • Pock, Thomas
  • TitelA higher-order MRF based variational model for multiplicative noise reduction
  • Datei
  • DOI10.1109/LSP.2014.2337274
  • Persistent Identifier
  • Erschienen inIEEE signal processing letters
  • Band21
  • Erscheinungsjahr2014
  • Heft11
  • Seiten1370-1374
  • LicenceCC-BY
  • ISSN1558-2361
  • ZugriffsrechteCC-BY
  • Download Statistik243
  • Peer ReviewJa
  • AbstractThe Fields of Experts (FoE) image prior model, a filter-based higher-order Markov Random Fields (MRF) model, has been shown to be effective for many image restoration problems. Motivated by the successes of FoE-based approaches, in this letter we propose a novel variational model for multiplicative noise reduction based on the FoE image prior model. The resulting model corresponds to a non-convex minimization problem, which can be efficiently solved by a recently published non-convex optimization algorithm. Experimental results based on synthetic speckle noise and real synthetic aperture radar (SAR) images suggest that the performance of our proposed method is on par with the best published despeckling algorithm. Besides, our proposed model comes along with an additional advantage, that the inference is extremely efficient. Our GPU based implementation takes less than 1s to produce state-of-the-art despeckling performance.