Image Inpainting: A Review
Abstract Although image inpainting, or the art of repairing the old and deteriorated images, has been around for many years, it has recently gained even more popularity, because of the recent development in image processing techniques. With the improvement of image processing tools and the flexibili...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Elharrouss, Omar [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2019 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2019 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Neural processing letters - Springer US, 1994, 51(2019), 2 vom: 06. Dez., Seite 2007-2028 |
|---|---|
| Übergeordnetes Werk: |
volume:51 ; year:2019 ; number:2 ; day:06 ; month:12 ; pages:2007-2028 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11063-019-10163-0 |
|---|
| Katalog-ID: |
OLC2044716194 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2044716194 | ||
| 003 | DE-627 | ||
| 005 | 20230504133739.0 | ||
| 007 | tu | ||
| 008 | 200819s2019 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11063-019-10163-0 |2 doi | |
| 035 | |a (DE-627)OLC2044716194 | ||
| 035 | |a (DE-He213)s11063-019-10163-0-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 000 |q VZ |
| 100 | 1 | |a Elharrouss, Omar |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Image Inpainting: A Review |
| 264 | 1 | |c 2019 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer Science+Business Media, LLC, part of Springer Nature 2019 | ||
| 520 | |a Abstract Although image inpainting, or the art of repairing the old and deteriorated images, has been around for many years, it has recently gained even more popularity, because of the recent development in image processing techniques. With the improvement of image processing tools and the flexibility of digital image editing, automatic image inpainting has found important applications in computer vision and has also become an important and challenging topic of research in image processing. This paper reviews the existing image inpainting approaches, that were classified into three subcategories, sequential-based, CNN-based, and GAN-based methods. In addition, for each category, a list of methods for different types of distortion on images are presented. Furthermore, the paper also presents available datasets. Last but not least, we present the results of real evaluations of the three categories of image inpainting methods performed on the used datasets, for different types of image distortion. We also present the evaluations metrics and discuss the performance of these methods in terms of these metrics. This overview can be used as a reference for image inpainting researchers, and it can also facilitate the comparison of the methods as well as the datasets used. The main contribution of this paper is the presentation of the three categories of image inpainting methods along with a list of available datasets that the researchers can use to evaluate their proposed methodology against. | ||
| 650 | 4 | |a Image inpainting | |
| 650 | 4 | |a Objects removal | |
| 650 | 4 | |a Image repairing | |
| 650 | 4 | |a CNN | |
| 650 | 4 | |a GAN | |
| 700 | 1 | |a Almaadeed, Noor |4 aut | |
| 700 | 1 | |a Al-Maadeed, Somaya |4 aut | |
| 700 | 1 | |a Akbari, Younes |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Neural processing letters |d Springer US, 1994 |g 51(2019), 2 vom: 06. Dez., Seite 2007-2028 |w (DE-627)198692617 |w (DE-600)1316823-X |w (DE-576)052842762 |x 1370-4621 |7 nnns |
| 773 | 1 | 8 | |g volume:51 |g year:2019 |g number:2 |g day:06 |g month:12 |g pages:2007-2028 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s11063-019-10163-0 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-PSY | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a GBV_ILN_70 | ||
| 951 | |a AR | ||
| 952 | |d 51 |j 2019 |e 2 |b 06 |c 12 |h 2007-2028 | ||
| author_variant |
o e oe n a na s a m sam y a ya |
|---|---|
| matchkey_str |
article:13704621:2019----::mgipitn |
| hierarchy_sort_str |
2019 |
| publishDate |
2019 |
| allfields |
10.1007/s11063-019-10163-0 doi (DE-627)OLC2044716194 (DE-He213)s11063-019-10163-0-p DE-627 ger DE-627 rakwb eng 000 VZ Elharrouss, Omar verfasserin aut Image Inpainting: A Review 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Although image inpainting, or the art of repairing the old and deteriorated images, has been around for many years, it has recently gained even more popularity, because of the recent development in image processing techniques. With the improvement of image processing tools and the flexibility of digital image editing, automatic image inpainting has found important applications in computer vision and has also become an important and challenging topic of research in image processing. This paper reviews the existing image inpainting approaches, that were classified into three subcategories, sequential-based, CNN-based, and GAN-based methods. In addition, for each category, a list of methods for different types of distortion on images are presented. Furthermore, the paper also presents available datasets. Last but not least, we present the results of real evaluations of the three categories of image inpainting methods performed on the used datasets, for different types of image distortion. We also present the evaluations metrics and discuss the performance of these methods in terms of these metrics. This overview can be used as a reference for image inpainting researchers, and it can also facilitate the comparison of the methods as well as the datasets used. The main contribution of this paper is the presentation of the three categories of image inpainting methods along with a list of available datasets that the researchers can use to evaluate their proposed methodology against. Image inpainting Objects removal Image repairing CNN GAN Almaadeed, Noor aut Al-Maadeed, Somaya aut Akbari, Younes aut Enthalten in Neural processing letters Springer US, 1994 51(2019), 2 vom: 06. Dez., Seite 2007-2028 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:51 year:2019 number:2 day:06 month:12 pages:2007-2028 https://doi.org/10.1007/s11063-019-10163-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT GBV_ILN_70 AR 51 2019 2 06 12 2007-2028 |
| spelling |
10.1007/s11063-019-10163-0 doi (DE-627)OLC2044716194 (DE-He213)s11063-019-10163-0-p DE-627 ger DE-627 rakwb eng 000 VZ Elharrouss, Omar verfasserin aut Image Inpainting: A Review 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Although image inpainting, or the art of repairing the old and deteriorated images, has been around for many years, it has recently gained even more popularity, because of the recent development in image processing techniques. With the improvement of image processing tools and the flexibility of digital image editing, automatic image inpainting has found important applications in computer vision and has also become an important and challenging topic of research in image processing. This paper reviews the existing image inpainting approaches, that were classified into three subcategories, sequential-based, CNN-based, and GAN-based methods. In addition, for each category, a list of methods for different types of distortion on images are presented. Furthermore, the paper also presents available datasets. Last but not least, we present the results of real evaluations of the three categories of image inpainting methods performed on the used datasets, for different types of image distortion. We also present the evaluations metrics and discuss the performance of these methods in terms of these metrics. This overview can be used as a reference for image inpainting researchers, and it can also facilitate the comparison of the methods as well as the datasets used. The main contribution of this paper is the presentation of the three categories of image inpainting methods along with a list of available datasets that the researchers can use to evaluate their proposed methodology against. Image inpainting Objects removal Image repairing CNN GAN Almaadeed, Noor aut Al-Maadeed, Somaya aut Akbari, Younes aut Enthalten in Neural processing letters Springer US, 1994 51(2019), 2 vom: 06. Dez., Seite 2007-2028 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:51 year:2019 number:2 day:06 month:12 pages:2007-2028 https://doi.org/10.1007/s11063-019-10163-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT GBV_ILN_70 AR 51 2019 2 06 12 2007-2028 |
| allfields_unstemmed |
10.1007/s11063-019-10163-0 doi (DE-627)OLC2044716194 (DE-He213)s11063-019-10163-0-p DE-627 ger DE-627 rakwb eng 000 VZ Elharrouss, Omar verfasserin aut Image Inpainting: A Review 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Although image inpainting, or the art of repairing the old and deteriorated images, has been around for many years, it has recently gained even more popularity, because of the recent development in image processing techniques. With the improvement of image processing tools and the flexibility of digital image editing, automatic image inpainting has found important applications in computer vision and has also become an important and challenging topic of research in image processing. This paper reviews the existing image inpainting approaches, that were classified into three subcategories, sequential-based, CNN-based, and GAN-based methods. In addition, for each category, a list of methods for different types of distortion on images are presented. Furthermore, the paper also presents available datasets. Last but not least, we present the results of real evaluations of the three categories of image inpainting methods performed on the used datasets, for different types of image distortion. We also present the evaluations metrics and discuss the performance of these methods in terms of these metrics. This overview can be used as a reference for image inpainting researchers, and it can also facilitate the comparison of the methods as well as the datasets used. The main contribution of this paper is the presentation of the three categories of image inpainting methods along with a list of available datasets that the researchers can use to evaluate their proposed methodology against. Image inpainting Objects removal Image repairing CNN GAN Almaadeed, Noor aut Al-Maadeed, Somaya aut Akbari, Younes aut Enthalten in Neural processing letters Springer US, 1994 51(2019), 2 vom: 06. Dez., Seite 2007-2028 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:51 year:2019 number:2 day:06 month:12 pages:2007-2028 https://doi.org/10.1007/s11063-019-10163-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT GBV_ILN_70 AR 51 2019 2 06 12 2007-2028 |
| allfieldsGer |
10.1007/s11063-019-10163-0 doi (DE-627)OLC2044716194 (DE-He213)s11063-019-10163-0-p DE-627 ger DE-627 rakwb eng 000 VZ Elharrouss, Omar verfasserin aut Image Inpainting: A Review 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Although image inpainting, or the art of repairing the old and deteriorated images, has been around for many years, it has recently gained even more popularity, because of the recent development in image processing techniques. With the improvement of image processing tools and the flexibility of digital image editing, automatic image inpainting has found important applications in computer vision and has also become an important and challenging topic of research in image processing. This paper reviews the existing image inpainting approaches, that were classified into three subcategories, sequential-based, CNN-based, and GAN-based methods. In addition, for each category, a list of methods for different types of distortion on images are presented. Furthermore, the paper also presents available datasets. Last but not least, we present the results of real evaluations of the three categories of image inpainting methods performed on the used datasets, for different types of image distortion. We also present the evaluations metrics and discuss the performance of these methods in terms of these metrics. This overview can be used as a reference for image inpainting researchers, and it can also facilitate the comparison of the methods as well as the datasets used. The main contribution of this paper is the presentation of the three categories of image inpainting methods along with a list of available datasets that the researchers can use to evaluate their proposed methodology against. Image inpainting Objects removal Image repairing CNN GAN Almaadeed, Noor aut Al-Maadeed, Somaya aut Akbari, Younes aut Enthalten in Neural processing letters Springer US, 1994 51(2019), 2 vom: 06. Dez., Seite 2007-2028 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:51 year:2019 number:2 day:06 month:12 pages:2007-2028 https://doi.org/10.1007/s11063-019-10163-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT GBV_ILN_70 AR 51 2019 2 06 12 2007-2028 |
| allfieldsSound |
10.1007/s11063-019-10163-0 doi (DE-627)OLC2044716194 (DE-He213)s11063-019-10163-0-p DE-627 ger DE-627 rakwb eng 000 VZ Elharrouss, Omar verfasserin aut Image Inpainting: A Review 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Although image inpainting, or the art of repairing the old and deteriorated images, has been around for many years, it has recently gained even more popularity, because of the recent development in image processing techniques. With the improvement of image processing tools and the flexibility of digital image editing, automatic image inpainting has found important applications in computer vision and has also become an important and challenging topic of research in image processing. This paper reviews the existing image inpainting approaches, that were classified into three subcategories, sequential-based, CNN-based, and GAN-based methods. In addition, for each category, a list of methods for different types of distortion on images are presented. Furthermore, the paper also presents available datasets. Last but not least, we present the results of real evaluations of the three categories of image inpainting methods performed on the used datasets, for different types of image distortion. We also present the evaluations metrics and discuss the performance of these methods in terms of these metrics. This overview can be used as a reference for image inpainting researchers, and it can also facilitate the comparison of the methods as well as the datasets used. The main contribution of this paper is the presentation of the three categories of image inpainting methods along with a list of available datasets that the researchers can use to evaluate their proposed methodology against. Image inpainting Objects removal Image repairing CNN GAN Almaadeed, Noor aut Al-Maadeed, Somaya aut Akbari, Younes aut Enthalten in Neural processing letters Springer US, 1994 51(2019), 2 vom: 06. Dez., Seite 2007-2028 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:51 year:2019 number:2 day:06 month:12 pages:2007-2028 https://doi.org/10.1007/s11063-019-10163-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT GBV_ILN_70 AR 51 2019 2 06 12 2007-2028 |
| language |
English |
| source |
Enthalten in Neural processing letters 51(2019), 2 vom: 06. Dez., Seite 2007-2028 volume:51 year:2019 number:2 day:06 month:12 pages:2007-2028 |
| sourceStr |
Enthalten in Neural processing letters 51(2019), 2 vom: 06. Dez., Seite 2007-2028 volume:51 year:2019 number:2 day:06 month:12 pages:2007-2028 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Image inpainting Objects removal Image repairing CNN GAN |
| dewey-raw |
000 |
| isfreeaccess_bool |
false |
| container_title |
Neural processing letters |
| authorswithroles_txt_mv |
Elharrouss, Omar @@aut@@ Almaadeed, Noor @@aut@@ Al-Maadeed, Somaya @@aut@@ Akbari, Younes @@aut@@ |
| publishDateDaySort_date |
2019-12-06T00:00:00Z |
| hierarchy_top_id |
198692617 |
| dewey-sort |
0 |
| id |
OLC2044716194 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2044716194</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504133739.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2019 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11063-019-10163-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2044716194</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11063-019-10163-0-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">000</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Elharrouss, Omar</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Image Inpainting: A Review</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC, part of Springer Nature 2019</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Although image inpainting, or the art of repairing the old and deteriorated images, has been around for many years, it has recently gained even more popularity, because of the recent development in image processing techniques. With the improvement of image processing tools and the flexibility of digital image editing, automatic image inpainting has found important applications in computer vision and has also become an important and challenging topic of research in image processing. This paper reviews the existing image inpainting approaches, that were classified into three subcategories, sequential-based, CNN-based, and GAN-based methods. In addition, for each category, a list of methods for different types of distortion on images are presented. Furthermore, the paper also presents available datasets. Last but not least, we present the results of real evaluations of the three categories of image inpainting methods performed on the used datasets, for different types of image distortion. We also present the evaluations metrics and discuss the performance of these methods in terms of these metrics. This overview can be used as a reference for image inpainting researchers, and it can also facilitate the comparison of the methods as well as the datasets used. The main contribution of this paper is the presentation of the three categories of image inpainting methods along with a list of available datasets that the researchers can use to evaluate their proposed methodology against.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image inpainting</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Objects removal</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image repairing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CNN</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">GAN</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Almaadeed, Noor</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Al-Maadeed, Somaya</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Akbari, Younes</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural processing letters</subfield><subfield code="d">Springer US, 1994</subfield><subfield code="g">51(2019), 2 vom: 06. Dez., Seite 2007-2028</subfield><subfield code="w">(DE-627)198692617</subfield><subfield code="w">(DE-600)1316823-X</subfield><subfield code="w">(DE-576)052842762</subfield><subfield code="x">1370-4621</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:51</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:2</subfield><subfield code="g">day:06</subfield><subfield code="g">month:12</subfield><subfield code="g">pages:2007-2028</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11063-019-10163-0</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PSY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">51</subfield><subfield code="j">2019</subfield><subfield code="e">2</subfield><subfield code="b">06</subfield><subfield code="c">12</subfield><subfield code="h">2007-2028</subfield></datafield></record></collection>
|
| author |
Elharrouss, Omar |
| spellingShingle |
Elharrouss, Omar ddc 000 misc Image inpainting misc Objects removal misc Image repairing misc CNN misc GAN Image Inpainting: A Review |
| authorStr |
Elharrouss, Omar |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)198692617 |
| format |
Article |
| dewey-ones |
000 - Computer science, information & general works |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
1370-4621 |
| topic_title |
000 VZ Image Inpainting: A Review Image inpainting Objects removal Image repairing CNN GAN |
| topic |
ddc 000 misc Image inpainting misc Objects removal misc Image repairing misc CNN misc GAN |
| topic_unstemmed |
ddc 000 misc Image inpainting misc Objects removal misc Image repairing misc CNN misc GAN |
| topic_browse |
ddc 000 misc Image inpainting misc Objects removal misc Image repairing misc CNN misc GAN |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Neural processing letters |
| hierarchy_parent_id |
198692617 |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Neural processing letters |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 |
| title |
Image Inpainting: A Review |
| ctrlnum |
(DE-627)OLC2044716194 (DE-He213)s11063-019-10163-0-p |
| title_full |
Image Inpainting: A Review |
| author_sort |
Elharrouss, Omar |
| journal |
Neural processing letters |
| journalStr |
Neural processing letters |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2019 |
| contenttype_str_mv |
txt |
| container_start_page |
2007 |
| author_browse |
Elharrouss, Omar Almaadeed, Noor Al-Maadeed, Somaya Akbari, Younes |
| container_volume |
51 |
| class |
000 VZ |
| format_se |
Aufsätze |
| author-letter |
Elharrouss, Omar |
| doi_str_mv |
10.1007/s11063-019-10163-0 |
| dewey-full |
000 |
| title_sort |
image inpainting: a review |
| title_auth |
Image Inpainting: A Review |
| abstract |
Abstract Although image inpainting, or the art of repairing the old and deteriorated images, has been around for many years, it has recently gained even more popularity, because of the recent development in image processing techniques. With the improvement of image processing tools and the flexibility of digital image editing, automatic image inpainting has found important applications in computer vision and has also become an important and challenging topic of research in image processing. This paper reviews the existing image inpainting approaches, that were classified into three subcategories, sequential-based, CNN-based, and GAN-based methods. In addition, for each category, a list of methods for different types of distortion on images are presented. Furthermore, the paper also presents available datasets. Last but not least, we present the results of real evaluations of the three categories of image inpainting methods performed on the used datasets, for different types of image distortion. We also present the evaluations metrics and discuss the performance of these methods in terms of these metrics. This overview can be used as a reference for image inpainting researchers, and it can also facilitate the comparison of the methods as well as the datasets used. The main contribution of this paper is the presentation of the three categories of image inpainting methods along with a list of available datasets that the researchers can use to evaluate their proposed methodology against. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
| abstractGer |
Abstract Although image inpainting, or the art of repairing the old and deteriorated images, has been around for many years, it has recently gained even more popularity, because of the recent development in image processing techniques. With the improvement of image processing tools and the flexibility of digital image editing, automatic image inpainting has found important applications in computer vision and has also become an important and challenging topic of research in image processing. This paper reviews the existing image inpainting approaches, that were classified into three subcategories, sequential-based, CNN-based, and GAN-based methods. In addition, for each category, a list of methods for different types of distortion on images are presented. Furthermore, the paper also presents available datasets. Last but not least, we present the results of real evaluations of the three categories of image inpainting methods performed on the used datasets, for different types of image distortion. We also present the evaluations metrics and discuss the performance of these methods in terms of these metrics. This overview can be used as a reference for image inpainting researchers, and it can also facilitate the comparison of the methods as well as the datasets used. The main contribution of this paper is the presentation of the three categories of image inpainting methods along with a list of available datasets that the researchers can use to evaluate their proposed methodology against. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract Although image inpainting, or the art of repairing the old and deteriorated images, has been around for many years, it has recently gained even more popularity, because of the recent development in image processing techniques. With the improvement of image processing tools and the flexibility of digital image editing, automatic image inpainting has found important applications in computer vision and has also become an important and challenging topic of research in image processing. This paper reviews the existing image inpainting approaches, that were classified into three subcategories, sequential-based, CNN-based, and GAN-based methods. In addition, for each category, a list of methods for different types of distortion on images are presented. Furthermore, the paper also presents available datasets. Last but not least, we present the results of real evaluations of the three categories of image inpainting methods performed on the used datasets, for different types of image distortion. We also present the evaluations metrics and discuss the performance of these methods in terms of these metrics. This overview can be used as a reference for image inpainting researchers, and it can also facilitate the comparison of the methods as well as the datasets used. The main contribution of this paper is the presentation of the three categories of image inpainting methods along with a list of available datasets that the researchers can use to evaluate their proposed methodology against. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT GBV_ILN_70 |
| container_issue |
2 |
| title_short |
Image Inpainting: A Review |
| url |
https://doi.org/10.1007/s11063-019-10163-0 |
| remote_bool |
false |
| author2 |
Almaadeed, Noor Al-Maadeed, Somaya Akbari, Younes |
| author2Str |
Almaadeed, Noor Al-Maadeed, Somaya Akbari, Younes |
| ppnlink |
198692617 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s11063-019-10163-0 |
| up_date |
2024-07-04T00:32:12.334Z |
| _version_ |
1803606433291304960 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2044716194</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504133739.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2019 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11063-019-10163-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2044716194</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11063-019-10163-0-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">000</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Elharrouss, Omar</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Image Inpainting: A Review</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC, part of Springer Nature 2019</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Although image inpainting, or the art of repairing the old and deteriorated images, has been around for many years, it has recently gained even more popularity, because of the recent development in image processing techniques. With the improvement of image processing tools and the flexibility of digital image editing, automatic image inpainting has found important applications in computer vision and has also become an important and challenging topic of research in image processing. This paper reviews the existing image inpainting approaches, that were classified into three subcategories, sequential-based, CNN-based, and GAN-based methods. In addition, for each category, a list of methods for different types of distortion on images are presented. Furthermore, the paper also presents available datasets. Last but not least, we present the results of real evaluations of the three categories of image inpainting methods performed on the used datasets, for different types of image distortion. We also present the evaluations metrics and discuss the performance of these methods in terms of these metrics. This overview can be used as a reference for image inpainting researchers, and it can also facilitate the comparison of the methods as well as the datasets used. The main contribution of this paper is the presentation of the three categories of image inpainting methods along with a list of available datasets that the researchers can use to evaluate their proposed methodology against.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image inpainting</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Objects removal</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image repairing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CNN</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">GAN</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Almaadeed, Noor</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Al-Maadeed, Somaya</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Akbari, Younes</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural processing letters</subfield><subfield code="d">Springer US, 1994</subfield><subfield code="g">51(2019), 2 vom: 06. Dez., Seite 2007-2028</subfield><subfield code="w">(DE-627)198692617</subfield><subfield code="w">(DE-600)1316823-X</subfield><subfield code="w">(DE-576)052842762</subfield><subfield code="x">1370-4621</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:51</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:2</subfield><subfield code="g">day:06</subfield><subfield code="g">month:12</subfield><subfield code="g">pages:2007-2028</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11063-019-10163-0</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PSY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">51</subfield><subfield code="j">2019</subfield><subfield code="e">2</subfield><subfield code="b">06</subfield><subfield code="c">12</subfield><subfield code="h">2007-2028</subfield></datafield></record></collection>
|
| score |
7.3998566 |