@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 = "2025, Feb. 12"
}