Group-based single image super-resolution with online dictionary learning

Abstract Recently, sparse representation has been successfully used in single image super-resolution reconstruction. Unlike the traditional single image super-resolution methods such as image interpolation, the super-resolution with sparse representation reconstructs image with one or several consta...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Lu, Xuan [verfasserIn] Wang, Dingwen [verfasserIn] Shi, Wenxuan [verfasserIn] Deng, Dexiang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Super-resolution Sparse representation Online dictionary learning Non-local similarity

Übergeordnetes Werk:	Enthalten in: EURASIP journal on advances in signal processing - Heidelberg : Springer, 2007, 2016(2016), 1 vom: 29. Juli
Übergeordnetes Werk:	volume:2016 ; year:2016 ; number:1 ; day:29 ; month:07

Links:	Volltext

DOI / URN:	10.1186/s13634-016-0380-9

Katalog-ID:	SPR032008406

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520			\|a Abstract Recently, sparse representation has been successfully used in single image super-resolution reconstruction. Unlike the traditional single image super-resolution methods such as image interpolation, the super-resolution with sparse representation reconstructs image with one or several constant dictionaries learned from external databases. However, the contents can vary significantly across different patches in a single image, and the fixed dictionaries cannot suit for every patch. This paper presents a novel approach for single image super-resolution based on sparse representation, which uses group as the basic unit, and trains dictionary with external database and the input low-resolution image itself for each group to ensure that the dictionary is suitable for the patches in the group. Simultaneous sparse coding algorithm is used to accelerate the processing and improve the result. Extensive experiments on natural images show that our method achieves better results than some state-of-the-art algorithms in terms of both objective and human visual evaluations.
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spelling	10.1186/s13634-016-0380-9 doi (DE-627)SPR032008406 (SPR)s13634-016-0380-9-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Lu, Xuan verfasserin aut Group-based single image super-resolution with online dictionary learning 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Recently, sparse representation has been successfully used in single image super-resolution reconstruction. Unlike the traditional single image super-resolution methods such as image interpolation, the super-resolution with sparse representation reconstructs image with one or several constant dictionaries learned from external databases. However, the contents can vary significantly across different patches in a single image, and the fixed dictionaries cannot suit for every patch. This paper presents a novel approach for single image super-resolution based on sparse representation, which uses group as the basic unit, and trains dictionary with external database and the input low-resolution image itself for each group to ensure that the dictionary is suitable for the patches in the group. Simultaneous sparse coding algorithm is used to accelerate the processing and improve the result. Extensive experiments on natural images show that our method achieves better results than some state-of-the-art algorithms in terms of both objective and human visual evaluations. Super-resolution (dpeaa)DE-He213 Sparse representation (dpeaa)DE-He213 Online dictionary learning (dpeaa)DE-He213 Non-local similarity (dpeaa)DE-He213 Wang, Dingwen verfasserin aut Shi, Wenxuan verfasserin aut Deng, Dexiang verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2016(2016), 1 vom: 29. Juli (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2016 year:2016 number:1 day:29 month:07 https://dx.doi.org/10.1186/s13634-016-0380-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2016 2016 1 29 07
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allfieldsGer	10.1186/s13634-016-0380-9 doi (DE-627)SPR032008406 (SPR)s13634-016-0380-9-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Lu, Xuan verfasserin aut Group-based single image super-resolution with online dictionary learning 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Recently, sparse representation has been successfully used in single image super-resolution reconstruction. Unlike the traditional single image super-resolution methods such as image interpolation, the super-resolution with sparse representation reconstructs image with one or several constant dictionaries learned from external databases. However, the contents can vary significantly across different patches in a single image, and the fixed dictionaries cannot suit for every patch. This paper presents a novel approach for single image super-resolution based on sparse representation, which uses group as the basic unit, and trains dictionary with external database and the input low-resolution image itself for each group to ensure that the dictionary is suitable for the patches in the group. Simultaneous sparse coding algorithm is used to accelerate the processing and improve the result. Extensive experiments on natural images show that our method achieves better results than some state-of-the-art algorithms in terms of both objective and human visual evaluations. Super-resolution (dpeaa)DE-He213 Sparse representation (dpeaa)DE-He213 Online dictionary learning (dpeaa)DE-He213 Non-local similarity (dpeaa)DE-He213 Wang, Dingwen verfasserin aut Shi, Wenxuan verfasserin aut Deng, Dexiang verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2016(2016), 1 vom: 29. Juli (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2016 year:2016 number:1 day:29 month:07 https://dx.doi.org/10.1186/s13634-016-0380-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2016 2016 1 29 07
allfieldsSound	10.1186/s13634-016-0380-9 doi (DE-627)SPR032008406 (SPR)s13634-016-0380-9-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Lu, Xuan verfasserin aut Group-based single image super-resolution with online dictionary learning 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Recently, sparse representation has been successfully used in single image super-resolution reconstruction. Unlike the traditional single image super-resolution methods such as image interpolation, the super-resolution with sparse representation reconstructs image with one or several constant dictionaries learned from external databases. However, the contents can vary significantly across different patches in a single image, and the fixed dictionaries cannot suit for every patch. This paper presents a novel approach for single image super-resolution based on sparse representation, which uses group as the basic unit, and trains dictionary with external database and the input low-resolution image itself for each group to ensure that the dictionary is suitable for the patches in the group. Simultaneous sparse coding algorithm is used to accelerate the processing and improve the result. Extensive experiments on natural images show that our method achieves better results than some state-of-the-art algorithms in terms of both objective and human visual evaluations. Super-resolution (dpeaa)DE-He213 Sparse representation (dpeaa)DE-He213 Online dictionary learning (dpeaa)DE-He213 Non-local similarity (dpeaa)DE-He213 Wang, Dingwen verfasserin aut Shi, Wenxuan verfasserin aut Deng, Dexiang verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2016(2016), 1 vom: 29. Juli (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2016 year:2016 number:1 day:29 month:07 https://dx.doi.org/10.1186/s13634-016-0380-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2016 2016 1 29 07
language	English
source	Enthalten in EURASIP journal on advances in signal processing 2016(2016), 1 vom: 29. Juli volume:2016 year:2016 number:1 day:29 month:07
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format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Super-resolution Sparse representation Online dictionary learning Non-local similarity
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container_title	EURASIP journal on advances in signal processing
authorswithroles_txt_mv	Lu, Xuan @@aut@@ Wang, Dingwen @@aut@@ Shi, Wenxuan @@aut@@ Deng, Dexiang @@aut@@
publishDateDaySort_date	2016-07-29T00:00:00Z
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author	Lu, Xuan
spellingShingle	Lu, Xuan ddc 620 bkl 53.73 misc Super-resolution misc Sparse representation misc Online dictionary learning misc Non-local similarity Group-based single image super-resolution with online dictionary learning
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topic_title	620 ASE 53.73 bkl Group-based single image super-resolution with online dictionary learning Super-resolution (dpeaa)DE-He213 Sparse representation (dpeaa)DE-He213 Online dictionary learning (dpeaa)DE-He213 Non-local similarity (dpeaa)DE-He213
topic	ddc 620 bkl 53.73 misc Super-resolution misc Sparse representation misc Online dictionary learning misc Non-local similarity
topic_unstemmed	ddc 620 bkl 53.73 misc Super-resolution misc Sparse representation misc Online dictionary learning misc Non-local similarity
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title	Group-based single image super-resolution with online dictionary learning
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title_full	Group-based single image super-resolution with online dictionary learning
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title_sort	group-based single image super-resolution with online dictionary learning
title_auth	Group-based single image super-resolution with online dictionary learning
abstract	Abstract Recently, sparse representation has been successfully used in single image super-resolution reconstruction. Unlike the traditional single image super-resolution methods such as image interpolation, the super-resolution with sparse representation reconstructs image with one or several constant dictionaries learned from external databases. However, the contents can vary significantly across different patches in a single image, and the fixed dictionaries cannot suit for every patch. This paper presents a novel approach for single image super-resolution based on sparse representation, which uses group as the basic unit, and trains dictionary with external database and the input low-resolution image itself for each group to ensure that the dictionary is suitable for the patches in the group. Simultaneous sparse coding algorithm is used to accelerate the processing and improve the result. Extensive experiments on natural images show that our method achieves better results than some state-of-the-art algorithms in terms of both objective and human visual evaluations.
abstractGer	Abstract Recently, sparse representation has been successfully used in single image super-resolution reconstruction. Unlike the traditional single image super-resolution methods such as image interpolation, the super-resolution with sparse representation reconstructs image with one or several constant dictionaries learned from external databases. However, the contents can vary significantly across different patches in a single image, and the fixed dictionaries cannot suit for every patch. This paper presents a novel approach for single image super-resolution based on sparse representation, which uses group as the basic unit, and trains dictionary with external database and the input low-resolution image itself for each group to ensure that the dictionary is suitable for the patches in the group. Simultaneous sparse coding algorithm is used to accelerate the processing and improve the result. Extensive experiments on natural images show that our method achieves better results than some state-of-the-art algorithms in terms of both objective and human visual evaluations.
abstract_unstemmed	Abstract Recently, sparse representation has been successfully used in single image super-resolution reconstruction. Unlike the traditional single image super-resolution methods such as image interpolation, the super-resolution with sparse representation reconstructs image with one or several constant dictionaries learned from external databases. However, the contents can vary significantly across different patches in a single image, and the fixed dictionaries cannot suit for every patch. This paper presents a novel approach for single image super-resolution based on sparse representation, which uses group as the basic unit, and trains dictionary with external database and the input low-resolution image itself for each group to ensure that the dictionary is suitable for the patches in the group. Simultaneous sparse coding algorithm is used to accelerate the processing and improve the result. Extensive experiments on natural images show that our method achieves better results than some state-of-the-art algorithms in terms of both objective and human visual evaluations.
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title_short	Group-based single image super-resolution with online dictionary learning
url	https://dx.doi.org/10.1186/s13634-016-0380-9
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author2	Wang, Dingwen Shi, Wenxuan Deng, Dexiang
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up_date	2024-07-04T02:08:01.072Z
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