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Image Inpainting Methods Evaluation and Improvement
With the upgrowing of digital processing of images and film archiving, the need for assisted or unsupervised restoration required the development of a series of methods and techniques. Among them, image inpainting is maybe the most impressive and useful. Based on partial derivative equations or text...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4127254/ https://www.ncbi.nlm.nih.gov/pubmed/25136700 http://dx.doi.org/10.1155/2014/937845 |
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author | Vreja, Raluca Brad, Remus |
author_facet | Vreja, Raluca Brad, Remus |
author_sort | Vreja, Raluca |
collection | PubMed |
description | With the upgrowing of digital processing of images and film archiving, the need for assisted or unsupervised restoration required the development of a series of methods and techniques. Among them, image inpainting is maybe the most impressive and useful. Based on partial derivative equations or texture synthesis, many other hybrid techniques have been proposed recently. The need for an analytical comparison, beside the visual one, urged us to perform the studies shown in the present paper. Starting with an overview of the domain, an evaluation of the five methods was performed using a common benchmark and measuring the PSNR. Conclusions regarding the performance of the investigated algorithms have been presented, categorizing them in function of the restored image structure. Based on these experiments, we have proposed an adaptation of Oliveira's and Hadhoud's algorithms, which are performing well on images with natural defects. |
format | Online Article Text |
id | pubmed-4127254 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-41272542014-08-18 Image Inpainting Methods Evaluation and Improvement Vreja, Raluca Brad, Remus ScientificWorldJournal Review Article With the upgrowing of digital processing of images and film archiving, the need for assisted or unsupervised restoration required the development of a series of methods and techniques. Among them, image inpainting is maybe the most impressive and useful. Based on partial derivative equations or texture synthesis, many other hybrid techniques have been proposed recently. The need for an analytical comparison, beside the visual one, urged us to perform the studies shown in the present paper. Starting with an overview of the domain, an evaluation of the five methods was performed using a common benchmark and measuring the PSNR. Conclusions regarding the performance of the investigated algorithms have been presented, categorizing them in function of the restored image structure. Based on these experiments, we have proposed an adaptation of Oliveira's and Hadhoud's algorithms, which are performing well on images with natural defects. Hindawi Publishing Corporation 2014 2014-07-17 /pmc/articles/PMC4127254/ /pubmed/25136700 http://dx.doi.org/10.1155/2014/937845 Text en Copyright © 2014 R. Vreja and R. Brad. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Article Vreja, Raluca Brad, Remus Image Inpainting Methods Evaluation and Improvement |
title | Image Inpainting Methods Evaluation and Improvement |
title_full | Image Inpainting Methods Evaluation and Improvement |
title_fullStr | Image Inpainting Methods Evaluation and Improvement |
title_full_unstemmed | Image Inpainting Methods Evaluation and Improvement |
title_short | Image Inpainting Methods Evaluation and Improvement |
title_sort | image inpainting methods evaluation and improvement |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4127254/ https://www.ncbi.nlm.nih.gov/pubmed/25136700 http://dx.doi.org/10.1155/2014/937845 |
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