An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution

Abstract In recent years, more and more attention has been paid to single image super-resolution reconstruction (SISR) by using deep learning networks. These networks have achieved good reconstruction results, but how to make better use of the feature information in the image, how to improve the net...
Ausführliche Beschreibung

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Autor*in:	Wei, Wang [verfasserIn] Yongbin, Jiang Yanhong, Luo Ji, Li Xin, Wang Tong, Zhang

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Deep residual dense network (DRDN) Single image super-resolution Fusion reconstruction Residual dense connection Multi-hop connection

Anmerkung:	© The Authors 2019

Übergeordnetes Werk:	Enthalten in: International journal of computational intelligence systems - Paris : Atlantis Press, 2008, 12(2019), 2 vom: Jan., Seite 1592-1601
Übergeordnetes Werk:	volume:12 ; year:2019 ; number:2 ; month:01 ; pages:1592-1601

Links:	Volltext

DOI / URN:	10.2991/ijcis.d.191209.001

Katalog-ID:	SPR054595681

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allfields	10.2991/ijcis.d.191209.001 doi (DE-627)SPR054595681 (SPR)ijcis.d.191209.001-e DE-627 ger DE-627 rakwb eng Wei, Wang verfasserin aut An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Authors 2019 Abstract In recent years, more and more attention has been paid to single image super-resolution reconstruction (SISR) by using deep learning networks. These networks have achieved good reconstruction results, but how to make better use of the feature information in the image, how to improve the network convergence speed, and so on still need further study. According to the above problems, a novel deep residual dense network (DRDN) is proposed in this paper. In detail, DRDN uses the residual-dense structure for local feature fusion, and finally carries out global residual fusion reconstruction. Residual-dense connection can make full use of the features of low-resolution images from shallow to deep layers, and provide more low-resolution image information for super-resolution reconstruction. Multi-hop connection can make errors spread to each layer of the network more quickly, which can alleviate the problem of difficult training caused by deepening network to a certain extent. The experiments show that DRDN not only ensure good training stability and successfully converge but also has less computing cost and higher reconstruction efficiency. Deep residual dense network (DRDN) (dpeaa)DE-He213 Single image super-resolution (dpeaa)DE-He213 Fusion reconstruction (dpeaa)DE-He213 Residual dense connection (dpeaa)DE-He213 Multi-hop connection (dpeaa)DE-He213 Yongbin, Jiang aut Yanhong, Luo aut Ji, Li aut Xin, Wang aut Tong, Zhang aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 12(2019), 2 vom: Jan., Seite 1592-1601 (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:12 year:2019 number:2 month:01 pages:1592-1601 https://dx.doi.org/10.2991/ijcis.d.191209.001 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_2055 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2019 2 01 1592-1601
spelling	10.2991/ijcis.d.191209.001 doi (DE-627)SPR054595681 (SPR)ijcis.d.191209.001-e DE-627 ger DE-627 rakwb eng Wei, Wang verfasserin aut An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Authors 2019 Abstract In recent years, more and more attention has been paid to single image super-resolution reconstruction (SISR) by using deep learning networks. These networks have achieved good reconstruction results, but how to make better use of the feature information in the image, how to improve the network convergence speed, and so on still need further study. According to the above problems, a novel deep residual dense network (DRDN) is proposed in this paper. In detail, DRDN uses the residual-dense structure for local feature fusion, and finally carries out global residual fusion reconstruction. Residual-dense connection can make full use of the features of low-resolution images from shallow to deep layers, and provide more low-resolution image information for super-resolution reconstruction. Multi-hop connection can make errors spread to each layer of the network more quickly, which can alleviate the problem of difficult training caused by deepening network to a certain extent. The experiments show that DRDN not only ensure good training stability and successfully converge but also has less computing cost and higher reconstruction efficiency. Deep residual dense network (DRDN) (dpeaa)DE-He213 Single image super-resolution (dpeaa)DE-He213 Fusion reconstruction (dpeaa)DE-He213 Residual dense connection (dpeaa)DE-He213 Multi-hop connection (dpeaa)DE-He213 Yongbin, Jiang aut Yanhong, Luo aut Ji, Li aut Xin, Wang aut Tong, Zhang aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 12(2019), 2 vom: Jan., Seite 1592-1601 (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:12 year:2019 number:2 month:01 pages:1592-1601 https://dx.doi.org/10.2991/ijcis.d.191209.001 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_2055 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2019 2 01 1592-1601
allfields_unstemmed	10.2991/ijcis.d.191209.001 doi (DE-627)SPR054595681 (SPR)ijcis.d.191209.001-e DE-627 ger DE-627 rakwb eng Wei, Wang verfasserin aut An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Authors 2019 Abstract In recent years, more and more attention has been paid to single image super-resolution reconstruction (SISR) by using deep learning networks. These networks have achieved good reconstruction results, but how to make better use of the feature information in the image, how to improve the network convergence speed, and so on still need further study. According to the above problems, a novel deep residual dense network (DRDN) is proposed in this paper. In detail, DRDN uses the residual-dense structure for local feature fusion, and finally carries out global residual fusion reconstruction. Residual-dense connection can make full use of the features of low-resolution images from shallow to deep layers, and provide more low-resolution image information for super-resolution reconstruction. Multi-hop connection can make errors spread to each layer of the network more quickly, which can alleviate the problem of difficult training caused by deepening network to a certain extent. The experiments show that DRDN not only ensure good training stability and successfully converge but also has less computing cost and higher reconstruction efficiency. Deep residual dense network (DRDN) (dpeaa)DE-He213 Single image super-resolution (dpeaa)DE-He213 Fusion reconstruction (dpeaa)DE-He213 Residual dense connection (dpeaa)DE-He213 Multi-hop connection (dpeaa)DE-He213 Yongbin, Jiang aut Yanhong, Luo aut Ji, Li aut Xin, Wang aut Tong, Zhang aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 12(2019), 2 vom: Jan., Seite 1592-1601 (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:12 year:2019 number:2 month:01 pages:1592-1601 https://dx.doi.org/10.2991/ijcis.d.191209.001 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_2055 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2019 2 01 1592-1601
allfieldsGer	10.2991/ijcis.d.191209.001 doi (DE-627)SPR054595681 (SPR)ijcis.d.191209.001-e DE-627 ger DE-627 rakwb eng Wei, Wang verfasserin aut An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Authors 2019 Abstract In recent years, more and more attention has been paid to single image super-resolution reconstruction (SISR) by using deep learning networks. These networks have achieved good reconstruction results, but how to make better use of the feature information in the image, how to improve the network convergence speed, and so on still need further study. According to the above problems, a novel deep residual dense network (DRDN) is proposed in this paper. In detail, DRDN uses the residual-dense structure for local feature fusion, and finally carries out global residual fusion reconstruction. Residual-dense connection can make full use of the features of low-resolution images from shallow to deep layers, and provide more low-resolution image information for super-resolution reconstruction. Multi-hop connection can make errors spread to each layer of the network more quickly, which can alleviate the problem of difficult training caused by deepening network to a certain extent. The experiments show that DRDN not only ensure good training stability and successfully converge but also has less computing cost and higher reconstruction efficiency. Deep residual dense network (DRDN) (dpeaa)DE-He213 Single image super-resolution (dpeaa)DE-He213 Fusion reconstruction (dpeaa)DE-He213 Residual dense connection (dpeaa)DE-He213 Multi-hop connection (dpeaa)DE-He213 Yongbin, Jiang aut Yanhong, Luo aut Ji, Li aut Xin, Wang aut Tong, Zhang aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 12(2019), 2 vom: Jan., Seite 1592-1601 (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:12 year:2019 number:2 month:01 pages:1592-1601 https://dx.doi.org/10.2991/ijcis.d.191209.001 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_2055 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2019 2 01 1592-1601
allfieldsSound	10.2991/ijcis.d.191209.001 doi (DE-627)SPR054595681 (SPR)ijcis.d.191209.001-e DE-627 ger DE-627 rakwb eng Wei, Wang verfasserin aut An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Authors 2019 Abstract In recent years, more and more attention has been paid to single image super-resolution reconstruction (SISR) by using deep learning networks. These networks have achieved good reconstruction results, but how to make better use of the feature information in the image, how to improve the network convergence speed, and so on still need further study. According to the above problems, a novel deep residual dense network (DRDN) is proposed in this paper. In detail, DRDN uses the residual-dense structure for local feature fusion, and finally carries out global residual fusion reconstruction. Residual-dense connection can make full use of the features of low-resolution images from shallow to deep layers, and provide more low-resolution image information for super-resolution reconstruction. Multi-hop connection can make errors spread to each layer of the network more quickly, which can alleviate the problem of difficult training caused by deepening network to a certain extent. The experiments show that DRDN not only ensure good training stability and successfully converge but also has less computing cost and higher reconstruction efficiency. Deep residual dense network (DRDN) (dpeaa)DE-He213 Single image super-resolution (dpeaa)DE-He213 Fusion reconstruction (dpeaa)DE-He213 Residual dense connection (dpeaa)DE-He213 Multi-hop connection (dpeaa)DE-He213 Yongbin, Jiang aut Yanhong, Luo aut Ji, Li aut Xin, Wang aut Tong, Zhang aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 12(2019), 2 vom: Jan., Seite 1592-1601 (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:12 year:2019 number:2 month:01 pages:1592-1601 https://dx.doi.org/10.2991/ijcis.d.191209.001 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_2055 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2019 2 01 1592-1601
language	English
source	Enthalten in International journal of computational intelligence systems 12(2019), 2 vom: Jan., Seite 1592-1601 volume:12 year:2019 number:2 month:01 pages:1592-1601
sourceStr	Enthalten in International journal of computational intelligence systems 12(2019), 2 vom: Jan., Seite 1592-1601 volume:12 year:2019 number:2 month:01 pages:1592-1601
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Deep residual dense network (DRDN) Single image super-resolution Fusion reconstruction Residual dense connection Multi-hop connection
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container_title	International journal of computational intelligence systems
authorswithroles_txt_mv	Wei, Wang @@aut@@ Yongbin, Jiang @@aut@@ Yanhong, Luo @@aut@@ Ji, Li @@aut@@ Xin, Wang @@aut@@ Tong, Zhang @@aut@@
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author	Wei, Wang
spellingShingle	Wei, Wang misc Deep residual dense network (DRDN) misc Single image super-resolution misc Fusion reconstruction misc Residual dense connection misc Multi-hop connection An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution
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topic_title	An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution Deep residual dense network (DRDN) (dpeaa)DE-He213 Single image super-resolution (dpeaa)DE-He213 Fusion reconstruction (dpeaa)DE-He213 Residual dense connection (dpeaa)DE-He213 Multi-hop connection (dpeaa)DE-He213
topic	misc Deep residual dense network (DRDN) misc Single image super-resolution misc Fusion reconstruction misc Residual dense connection misc Multi-hop connection
topic_unstemmed	misc Deep residual dense network (DRDN) misc Single image super-resolution misc Fusion reconstruction misc Residual dense connection misc Multi-hop connection
topic_browse	misc Deep residual dense network (DRDN) misc Single image super-resolution misc Fusion reconstruction misc Residual dense connection misc Multi-hop connection
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title	An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution
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title_full	An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution
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author_browse	Wei, Wang Yongbin, Jiang Yanhong, Luo Ji, Li Xin, Wang Tong, Zhang
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title_sort	advanced deep residual dense network (drdn) approach for image super-resolution
title_auth	An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution
abstract	Abstract In recent years, more and more attention has been paid to single image super-resolution reconstruction (SISR) by using deep learning networks. These networks have achieved good reconstruction results, but how to make better use of the feature information in the image, how to improve the network convergence speed, and so on still need further study. According to the above problems, a novel deep residual dense network (DRDN) is proposed in this paper. In detail, DRDN uses the residual-dense structure for local feature fusion, and finally carries out global residual fusion reconstruction. Residual-dense connection can make full use of the features of low-resolution images from shallow to deep layers, and provide more low-resolution image information for super-resolution reconstruction. Multi-hop connection can make errors spread to each layer of the network more quickly, which can alleviate the problem of difficult training caused by deepening network to a certain extent. The experiments show that DRDN not only ensure good training stability and successfully converge but also has less computing cost and higher reconstruction efficiency. © The Authors 2019
abstractGer	Abstract In recent years, more and more attention has been paid to single image super-resolution reconstruction (SISR) by using deep learning networks. These networks have achieved good reconstruction results, but how to make better use of the feature information in the image, how to improve the network convergence speed, and so on still need further study. According to the above problems, a novel deep residual dense network (DRDN) is proposed in this paper. In detail, DRDN uses the residual-dense structure for local feature fusion, and finally carries out global residual fusion reconstruction. Residual-dense connection can make full use of the features of low-resolution images from shallow to deep layers, and provide more low-resolution image information for super-resolution reconstruction. Multi-hop connection can make errors spread to each layer of the network more quickly, which can alleviate the problem of difficult training caused by deepening network to a certain extent. The experiments show that DRDN not only ensure good training stability and successfully converge but also has less computing cost and higher reconstruction efficiency. © The Authors 2019
abstract_unstemmed	Abstract In recent years, more and more attention has been paid to single image super-resolution reconstruction (SISR) by using deep learning networks. These networks have achieved good reconstruction results, but how to make better use of the feature information in the image, how to improve the network convergence speed, and so on still need further study. According to the above problems, a novel deep residual dense network (DRDN) is proposed in this paper. In detail, DRDN uses the residual-dense structure for local feature fusion, and finally carries out global residual fusion reconstruction. Residual-dense connection can make full use of the features of low-resolution images from shallow to deep layers, and provide more low-resolution image information for super-resolution reconstruction. Multi-hop connection can make errors spread to each layer of the network more quickly, which can alleviate the problem of difficult training caused by deepening network to a certain extent. The experiments show that DRDN not only ensure good training stability and successfully converge but also has less computing cost and higher reconstruction efficiency. © The Authors 2019
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title_short	An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution
url	https://dx.doi.org/10.2991/ijcis.d.191209.001
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author2	Yongbin, Jiang Yanhong, Luo Ji, Li Xin, Wang Tong, Zhang
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up_date	2024-07-04T02:19:08.182Z
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