@InProceedings{LevadaMascTann:2009:NoItAp,
               author = "Levada, Alexandre L. M. and Mascarenhas, Nelson Delfino 
                         d'{\'A}vila and Tann{\'u}s, Alberto",
          affiliation = "{Universidade de S{\~a}o Paulo} and {Universidade Federal de 
                         S{\~a}o Carlos} and {Universidade de S{\~a}o Paulo}",
                title = "GSAShrink: A Novel Iterative Approach for Wavelet-Based Image 
                         Denoising",
            booktitle = "Proceedings...",
                 year = "2009",
               editor = "Nonato, Luis Gustavo and Scharcanski, Jacob",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 22. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Image Denoising, Wavelets, Bayesian Estimation, Maximum a 
                         Posteriori, Game Strategy Approach.",
             abstract = "In this paper we propose a novel iterative algorithm for 
                         wavelet-based image denoising following a Maximum a Posteriori 
                         (MAP) approach. The wavelet shrinkage problem is modeled according 
                         to the Bayesian paradigm, providing a strong and extremely 
                         flexible framework for solving general image denoising problems. 
                         To approximate the MAP estimator, we propose GSAShrink, a modified 
                         version of a known combinatorial optimization algorithm based on 
                         non-cooperative game theory (Game Strategy Approach, or GSA). In 
                         order to modify the original algorithm to our purposes, we 
                         generalize GSA by introducing some additional control parameters 
                         and steps to reflect the nature of wavelet shrinkage applications. 
                         To test and evaluate the proposed method, experiments using 
                         several wavelet basis on noisy images are proposed. Additionally 
                         to better visual quality, the obtained results produce 
                         quantitative metrics (MSE, PSNR, ISNR and UIQ) that show 
                         significant improvements in comparison to traditional wavelet 
                         denoising approaches known as soft and hard thresholding, 
                         indicating the effectiveness of the proposed algorithm.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "11-14 Oct. 2009",
                  doi = "10.1109/SIBGRAPI.2009.8",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2009.8",
             language = "en",
                  ibi = "8JMKD3MGPBW4/35S5N8H",
                  url = "http://urlib.net/ibi/8JMKD3MGPBW4/35S5N8H",
           targetfile = "GSAShrink_Sibgrapi2009.pdf",
        urlaccessdate = "2024, May 02"
}