@InProceedings{Levada:2021:NoMeFi,
               author = "Levada, Alexandre L. M.",
          affiliation = "Computing Department, Federal University of S{\~a}o Carlos",
                title = "Non-local medians filter for joint Gaussian and impulsive image 
                         denoising",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Image denoising, Non-local Medians, KL-divergence, impulsive 
                         noise.",
             abstract = "Image denoising concerns with the development of filters to remove 
                         or attenuate random perturbations in the observed data, but at the 
                         same time, preserving most of edges and fine details in the scene. 
                         One problem with joint additive Gaussian and impulsive noise 
                         degradation is that they are spread over all frequencies of the 
                         signal. Hence, the most effective filters for this kind of noise 
                         are implemented in the spatial domain. In this paper, we proposed 
                         a Non-Local Medians filter that combine the medians of every patch 
                         of a search window using two distinct similarity measures: the 
                         Euclidean distance and the Kullback-Leibler divergence between 
                         Gaussian densities estimated from the patches. Computational 
                         experiments with 25 images corrupted by joint Gaussian and 
                         impulsive noises show that the proposed method is capable of 
                         producing, on average, significant higher PSNR and SSIM than the 
                         combination of the median filter and the Non-Local Means filter 
                         applied independently.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00029",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00029",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45ALTQ2",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45ALTQ2",
           targetfile = "example.pdf",
        urlaccessdate = "2024, May 06"
}