@InProceedings{BarrientosFernFern:2020:ReImIn,
               author = "Barrientos, David and Fernandes, Bruno and Fernandes, Sergio",
          affiliation = "Universidade de Pernambuco, Brasil and Universidade de Pernambuco, 
                         Brasil and Universidade de Pernambuco, Brasil",
                title = "A review on image inpainting techniques and datasets",
            booktitle = "Proceedings...",
                 year = "2020",
               editor = "Musse, Soraia Raupp and Cesar Junior, Roberto Marcondes and 
                         Pelechano, Nuria and Wang, Zhangyang (Atlas)",
         organization = "Conference on Graphics, Patterns and Images, 33. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "convolution-based, dataset, deep-learning, diffusion-based, 
                         inpainting, patch-based, reconstruction.",
             abstract = "Image inpainting is a process that allows filling in target 
                         regions with alternative contents by estimating the suitable 
                         information from auxiliary data, either from surrounding areas or 
                         external sources. Digital image inpainting techniques are 
                         classified in traditional techniques and Deep Learning techniques. 
                         Traditional techniques are able to produce accurate high-quality 
                         results when the missing areas are small, however none of them are 
                         able to generate novel objects not found in the source image 
                         neither to produce semantically consistent results. Deep Learning 
                         techniques have greatly improved the quality on image inpainting 
                         delivering promising results by generating semantic hole filling 
                         and novel objects not found in the original image. However, there 
                         is still a lot of room for improvement, specially on arbitrary 
                         image sizes, arbitrary masks, high resolution texture synthesis, 
                         reduction of computation resources and reduction of training time. 
                         This work classifies and orders chronologically the most prominent 
                         techniques, providing an overall explanation on its operation. It 
                         presents, as well, the most used datasets and evaluation metrics 
                         across all the works reviewed.",
  conference-location = "Porto de Galinhas (virtual)",
      conference-year = "7-10 Nov. 2020",
                  doi = "10.1109/SIBGRAPI51738.2020.00040",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI51738.2020.00040",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/43B46NS",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/43B46NS",
           targetfile = "89 - A Review on Image Inpainting Techniques and Datasets.pdf",
        urlaccessdate = "2024, May 02"
}