Medical image super-resolution via deep residual neural network in the shearlet domain
Abstract This paper proposes aconvolutional neural network (CNN)-based efficient medical image super-resolution (SR) method in the shearlet domain. Because of differences between imaging mechanisms optimized for natural images and medical images, the design begins with building a medical image datas...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Chunpeng [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2021 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 80(2021), 17 vom: 06. Mai, Seite 26637-26655 |
|---|---|
| Übergeordnetes Werk: |
volume:80 ; year:2021 ; number:17 ; day:06 ; month:05 ; pages:26637-26655 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11042-021-10894-0 |
|---|
| Katalog-ID: |
OLC2126484629 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2126484629 | ||
| 003 | DE-627 | ||
| 005 | 20230505115126.0 | ||
| 007 | tu | ||
| 008 | 230505s2021 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11042-021-10894-0 |2 doi | |
| 035 | |a (DE-627)OLC2126484629 | ||
| 035 | |a (DE-He213)s11042-021-10894-0-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 070 |a 004 |q VZ |
| 100 | 1 | |a Wang, Chunpeng |e verfasserin |0 (orcid)0000-0002-3742-5614 |4 aut | |
| 245 | 1 | 0 | |a Medical image super-resolution via deep residual neural network in the shearlet domain |
| 264 | 1 | |c 2021 | |
| 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 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 | ||
| 520 | |a Abstract This paper proposes aconvolutional neural network (CNN)-based efficient medical image super-resolution (SR) method in the shearlet domain. Because of differences between imaging mechanisms optimized for natural images and medical images, the design begins with building a medical image dataset for medical image SR and extracting effective areas to remarkably enhance the training effects of the CNN-based method. Then, a new medical image SR network structure—deep medical super-resolution network (DMSRN)—has been designed in which local residual learning is implemented through a recursive network and combined with global residual learning to heighten the depth of the network on the ground with no parameter increase. This effectively fixes the long-term dependency problem, which causes the prior state layers to barely have any effect on the following state layers. Last, the design addresses the problem of too-smooth reconstruction effects in the CNN-based method in the image space domain; shearlet transform is introduced to DMSRN to restore global topology through low-frequency sub-bands and restore local edge detail information through high-frequency sub-bands. Experimental results show that the proposed method is better than other state-of-the-art methods for medical image SR, which significantly promotes the restoration ability of texture structure and edge details. | ||
| 650 | 4 | |a Deep medical super-resolution network (DMSRN) | |
| 650 | 4 | |a Medical image | |
| 650 | 4 | |a Super-resolution | |
| 650 | 4 | |a Shearlet domain | |
| 700 | 1 | |a Wang, Simiao |4 aut | |
| 700 | 1 | |a Xia, Zhiqiu |4 aut | |
| 700 | 1 | |a Li, Qi |4 aut | |
| 700 | 1 | |a Ma, Bin |4 aut | |
| 700 | 1 | |a Li, Jian |4 aut | |
| 700 | 1 | |a Yang, Meihong |4 aut | |
| 700 | 1 | |a Shi, Yun-Qing |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Multimedia tools and applications |d Springer US, 1995 |g 80(2021), 17 vom: 06. Mai, Seite 26637-26655 |w (DE-627)189064145 |w (DE-600)1287642-2 |w (DE-576)052842126 |x 1380-7501 |7 nnns |
| 773 | 1 | 8 | |g volume:80 |g year:2021 |g number:17 |g day:06 |g month:05 |g pages:26637-26655 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s11042-021-10894-0 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a SSG-OLC-BUB | ||
| 912 | |a SSG-OLC-MKW | ||
| 951 | |a AR | ||
| 952 | |d 80 |j 2021 |e 17 |b 06 |c 05 |h 26637-26655 | ||
| author_variant |
c w cw s w sw z x zx q l ql b m bm j l jl m y my y q s yqs |
|---|---|
| matchkey_str |
article:13807501:2021----::eiaiaeuersltovaepeiulerlewr |
| hierarchy_sort_str |
2021 |
| publishDate |
2021 |
| allfields |
10.1007/s11042-021-10894-0 doi (DE-627)OLC2126484629 (DE-He213)s11042-021-10894-0-p DE-627 ger DE-627 rakwb eng 070 004 VZ Wang, Chunpeng verfasserin (orcid)0000-0002-3742-5614 aut Medical image super-resolution via deep residual neural network in the shearlet domain 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract This paper proposes aconvolutional neural network (CNN)-based efficient medical image super-resolution (SR) method in the shearlet domain. Because of differences between imaging mechanisms optimized for natural images and medical images, the design begins with building a medical image dataset for medical image SR and extracting effective areas to remarkably enhance the training effects of the CNN-based method. Then, a new medical image SR network structure—deep medical super-resolution network (DMSRN)—has been designed in which local residual learning is implemented through a recursive network and combined with global residual learning to heighten the depth of the network on the ground with no parameter increase. This effectively fixes the long-term dependency problem, which causes the prior state layers to barely have any effect on the following state layers. Last, the design addresses the problem of too-smooth reconstruction effects in the CNN-based method in the image space domain; shearlet transform is introduced to DMSRN to restore global topology through low-frequency sub-bands and restore local edge detail information through high-frequency sub-bands. Experimental results show that the proposed method is better than other state-of-the-art methods for medical image SR, which significantly promotes the restoration ability of texture structure and edge details. Deep medical super-resolution network (DMSRN) Medical image Super-resolution Shearlet domain Wang, Simiao aut Xia, Zhiqiu aut Li, Qi aut Ma, Bin aut Li, Jian aut Yang, Meihong aut Shi, Yun-Qing aut Enthalten in Multimedia tools and applications Springer US, 1995 80(2021), 17 vom: 06. Mai, Seite 26637-26655 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:80 year:2021 number:17 day:06 month:05 pages:26637-26655 https://doi.org/10.1007/s11042-021-10894-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 80 2021 17 06 05 26637-26655 |
| spelling |
10.1007/s11042-021-10894-0 doi (DE-627)OLC2126484629 (DE-He213)s11042-021-10894-0-p DE-627 ger DE-627 rakwb eng 070 004 VZ Wang, Chunpeng verfasserin (orcid)0000-0002-3742-5614 aut Medical image super-resolution via deep residual neural network in the shearlet domain 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract This paper proposes aconvolutional neural network (CNN)-based efficient medical image super-resolution (SR) method in the shearlet domain. Because of differences between imaging mechanisms optimized for natural images and medical images, the design begins with building a medical image dataset for medical image SR and extracting effective areas to remarkably enhance the training effects of the CNN-based method. Then, a new medical image SR network structure—deep medical super-resolution network (DMSRN)—has been designed in which local residual learning is implemented through a recursive network and combined with global residual learning to heighten the depth of the network on the ground with no parameter increase. This effectively fixes the long-term dependency problem, which causes the prior state layers to barely have any effect on the following state layers. Last, the design addresses the problem of too-smooth reconstruction effects in the CNN-based method in the image space domain; shearlet transform is introduced to DMSRN to restore global topology through low-frequency sub-bands and restore local edge detail information through high-frequency sub-bands. Experimental results show that the proposed method is better than other state-of-the-art methods for medical image SR, which significantly promotes the restoration ability of texture structure and edge details. Deep medical super-resolution network (DMSRN) Medical image Super-resolution Shearlet domain Wang, Simiao aut Xia, Zhiqiu aut Li, Qi aut Ma, Bin aut Li, Jian aut Yang, Meihong aut Shi, Yun-Qing aut Enthalten in Multimedia tools and applications Springer US, 1995 80(2021), 17 vom: 06. Mai, Seite 26637-26655 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:80 year:2021 number:17 day:06 month:05 pages:26637-26655 https://doi.org/10.1007/s11042-021-10894-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 80 2021 17 06 05 26637-26655 |
| allfields_unstemmed |
10.1007/s11042-021-10894-0 doi (DE-627)OLC2126484629 (DE-He213)s11042-021-10894-0-p DE-627 ger DE-627 rakwb eng 070 004 VZ Wang, Chunpeng verfasserin (orcid)0000-0002-3742-5614 aut Medical image super-resolution via deep residual neural network in the shearlet domain 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract This paper proposes aconvolutional neural network (CNN)-based efficient medical image super-resolution (SR) method in the shearlet domain. Because of differences between imaging mechanisms optimized for natural images and medical images, the design begins with building a medical image dataset for medical image SR and extracting effective areas to remarkably enhance the training effects of the CNN-based method. Then, a new medical image SR network structure—deep medical super-resolution network (DMSRN)—has been designed in which local residual learning is implemented through a recursive network and combined with global residual learning to heighten the depth of the network on the ground with no parameter increase. This effectively fixes the long-term dependency problem, which causes the prior state layers to barely have any effect on the following state layers. Last, the design addresses the problem of too-smooth reconstruction effects in the CNN-based method in the image space domain; shearlet transform is introduced to DMSRN to restore global topology through low-frequency sub-bands and restore local edge detail information through high-frequency sub-bands. Experimental results show that the proposed method is better than other state-of-the-art methods for medical image SR, which significantly promotes the restoration ability of texture structure and edge details. Deep medical super-resolution network (DMSRN) Medical image Super-resolution Shearlet domain Wang, Simiao aut Xia, Zhiqiu aut Li, Qi aut Ma, Bin aut Li, Jian aut Yang, Meihong aut Shi, Yun-Qing aut Enthalten in Multimedia tools and applications Springer US, 1995 80(2021), 17 vom: 06. Mai, Seite 26637-26655 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:80 year:2021 number:17 day:06 month:05 pages:26637-26655 https://doi.org/10.1007/s11042-021-10894-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 80 2021 17 06 05 26637-26655 |
| allfieldsGer |
10.1007/s11042-021-10894-0 doi (DE-627)OLC2126484629 (DE-He213)s11042-021-10894-0-p DE-627 ger DE-627 rakwb eng 070 004 VZ Wang, Chunpeng verfasserin (orcid)0000-0002-3742-5614 aut Medical image super-resolution via deep residual neural network in the shearlet domain 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract This paper proposes aconvolutional neural network (CNN)-based efficient medical image super-resolution (SR) method in the shearlet domain. Because of differences between imaging mechanisms optimized for natural images and medical images, the design begins with building a medical image dataset for medical image SR and extracting effective areas to remarkably enhance the training effects of the CNN-based method. Then, a new medical image SR network structure—deep medical super-resolution network (DMSRN)—has been designed in which local residual learning is implemented through a recursive network and combined with global residual learning to heighten the depth of the network on the ground with no parameter increase. This effectively fixes the long-term dependency problem, which causes the prior state layers to barely have any effect on the following state layers. Last, the design addresses the problem of too-smooth reconstruction effects in the CNN-based method in the image space domain; shearlet transform is introduced to DMSRN to restore global topology through low-frequency sub-bands and restore local edge detail information through high-frequency sub-bands. Experimental results show that the proposed method is better than other state-of-the-art methods for medical image SR, which significantly promotes the restoration ability of texture structure and edge details. Deep medical super-resolution network (DMSRN) Medical image Super-resolution Shearlet domain Wang, Simiao aut Xia, Zhiqiu aut Li, Qi aut Ma, Bin aut Li, Jian aut Yang, Meihong aut Shi, Yun-Qing aut Enthalten in Multimedia tools and applications Springer US, 1995 80(2021), 17 vom: 06. Mai, Seite 26637-26655 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:80 year:2021 number:17 day:06 month:05 pages:26637-26655 https://doi.org/10.1007/s11042-021-10894-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 80 2021 17 06 05 26637-26655 |
| allfieldsSound |
10.1007/s11042-021-10894-0 doi (DE-627)OLC2126484629 (DE-He213)s11042-021-10894-0-p DE-627 ger DE-627 rakwb eng 070 004 VZ Wang, Chunpeng verfasserin (orcid)0000-0002-3742-5614 aut Medical image super-resolution via deep residual neural network in the shearlet domain 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract This paper proposes aconvolutional neural network (CNN)-based efficient medical image super-resolution (SR) method in the shearlet domain. Because of differences between imaging mechanisms optimized for natural images and medical images, the design begins with building a medical image dataset for medical image SR and extracting effective areas to remarkably enhance the training effects of the CNN-based method. Then, a new medical image SR network structure—deep medical super-resolution network (DMSRN)—has been designed in which local residual learning is implemented through a recursive network and combined with global residual learning to heighten the depth of the network on the ground with no parameter increase. This effectively fixes the long-term dependency problem, which causes the prior state layers to barely have any effect on the following state layers. Last, the design addresses the problem of too-smooth reconstruction effects in the CNN-based method in the image space domain; shearlet transform is introduced to DMSRN to restore global topology through low-frequency sub-bands and restore local edge detail information through high-frequency sub-bands. Experimental results show that the proposed method is better than other state-of-the-art methods for medical image SR, which significantly promotes the restoration ability of texture structure and edge details. Deep medical super-resolution network (DMSRN) Medical image Super-resolution Shearlet domain Wang, Simiao aut Xia, Zhiqiu aut Li, Qi aut Ma, Bin aut Li, Jian aut Yang, Meihong aut Shi, Yun-Qing aut Enthalten in Multimedia tools and applications Springer US, 1995 80(2021), 17 vom: 06. Mai, Seite 26637-26655 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:80 year:2021 number:17 day:06 month:05 pages:26637-26655 https://doi.org/10.1007/s11042-021-10894-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 80 2021 17 06 05 26637-26655 |
| language |
English |
| source |
Enthalten in Multimedia tools and applications 80(2021), 17 vom: 06. Mai, Seite 26637-26655 volume:80 year:2021 number:17 day:06 month:05 pages:26637-26655 |
| sourceStr |
Enthalten in Multimedia tools and applications 80(2021), 17 vom: 06. Mai, Seite 26637-26655 volume:80 year:2021 number:17 day:06 month:05 pages:26637-26655 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Deep medical super-resolution network (DMSRN) Medical image Super-resolution Shearlet domain |
| dewey-raw |
070 |
| isfreeaccess_bool |
false |
| container_title |
Multimedia tools and applications |
| authorswithroles_txt_mv |
Wang, Chunpeng @@aut@@ Wang, Simiao @@aut@@ Xia, Zhiqiu @@aut@@ Li, Qi @@aut@@ Ma, Bin @@aut@@ Li, Jian @@aut@@ Yang, Meihong @@aut@@ Shi, Yun-Qing @@aut@@ |
| publishDateDaySort_date |
2021-05-06T00:00:00Z |
| hierarchy_top_id |
189064145 |
| dewey-sort |
270 |
| id |
OLC2126484629 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2126484629</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505115126.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230505s2021 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11042-021-10894-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2126484629</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11042-021-10894-0-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">070</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wang, Chunpeng</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-3742-5614</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Medical image super-resolution via deep residual neural network in the shearlet domain</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</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">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper proposes aconvolutional neural network (CNN)-based efficient medical image super-resolution (SR) method in the shearlet domain. Because of differences between imaging mechanisms optimized for natural images and medical images, the design begins with building a medical image dataset for medical image SR and extracting effective areas to remarkably enhance the training effects of the CNN-based method. Then, a new medical image SR network structure—deep medical super-resolution network (DMSRN)—has been designed in which local residual learning is implemented through a recursive network and combined with global residual learning to heighten the depth of the network on the ground with no parameter increase. This effectively fixes the long-term dependency problem, which causes the prior state layers to barely have any effect on the following state layers. Last, the design addresses the problem of too-smooth reconstruction effects in the CNN-based method in the image space domain; shearlet transform is introduced to DMSRN to restore global topology through low-frequency sub-bands and restore local edge detail information through high-frequency sub-bands. Experimental results show that the proposed method is better than other state-of-the-art methods for medical image SR, which significantly promotes the restoration ability of texture structure and edge details.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep medical super-resolution network (DMSRN)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Medical image</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Super-resolution</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Shearlet domain</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Simiao</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xia, Zhiqiu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Qi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ma, Bin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Jian</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Meihong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shi, Yun-Qing</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multimedia tools and applications</subfield><subfield code="d">Springer US, 1995</subfield><subfield code="g">80(2021), 17 vom: 06. Mai, Seite 26637-26655</subfield><subfield code="w">(DE-627)189064145</subfield><subfield code="w">(DE-600)1287642-2</subfield><subfield code="w">(DE-576)052842126</subfield><subfield code="x">1380-7501</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:80</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:17</subfield><subfield code="g">day:06</subfield><subfield code="g">month:05</subfield><subfield code="g">pages:26637-26655</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11042-021-10894-0</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-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MKW</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">80</subfield><subfield code="j">2021</subfield><subfield code="e">17</subfield><subfield code="b">06</subfield><subfield code="c">05</subfield><subfield code="h">26637-26655</subfield></datafield></record></collection>
|
| author |
Wang, Chunpeng |
| spellingShingle |
Wang, Chunpeng ddc 070 misc Deep medical super-resolution network (DMSRN) misc Medical image misc Super-resolution misc Shearlet domain Medical image super-resolution via deep residual neural network in the shearlet domain |
| authorStr |
Wang, Chunpeng |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)189064145 |
| format |
Article |
| dewey-ones |
070 - News media, journalism & publishing 004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
1380-7501 |
| topic_title |
070 004 VZ Medical image super-resolution via deep residual neural network in the shearlet domain Deep medical super-resolution network (DMSRN) Medical image Super-resolution Shearlet domain |
| topic |
ddc 070 misc Deep medical super-resolution network (DMSRN) misc Medical image misc Super-resolution misc Shearlet domain |
| topic_unstemmed |
ddc 070 misc Deep medical super-resolution network (DMSRN) misc Medical image misc Super-resolution misc Shearlet domain |
| topic_browse |
ddc 070 misc Deep medical super-resolution network (DMSRN) misc Medical image misc Super-resolution misc Shearlet domain |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Multimedia tools and applications |
| hierarchy_parent_id |
189064145 |
| dewey-tens |
070 - News media, journalism & publishing 000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Multimedia tools and applications |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 |
| title |
Medical image super-resolution via deep residual neural network in the shearlet domain |
| ctrlnum |
(DE-627)OLC2126484629 (DE-He213)s11042-021-10894-0-p |
| title_full |
Medical image super-resolution via deep residual neural network in the shearlet domain |
| author_sort |
Wang, Chunpeng |
| journal |
Multimedia tools and applications |
| journalStr |
Multimedia tools and applications |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2021 |
| contenttype_str_mv |
txt |
| container_start_page |
26637 |
| author_browse |
Wang, Chunpeng Wang, Simiao Xia, Zhiqiu Li, Qi Ma, Bin Li, Jian Yang, Meihong Shi, Yun-Qing |
| container_volume |
80 |
| class |
070 004 VZ |
| format_se |
Aufsätze |
| author-letter |
Wang, Chunpeng |
| doi_str_mv |
10.1007/s11042-021-10894-0 |
| normlink |
(ORCID)0000-0002-3742-5614 |
| normlink_prefix_str_mv |
(orcid)0000-0002-3742-5614 |
| dewey-full |
070 004 |
| title_sort |
medical image super-resolution via deep residual neural network in the shearlet domain |
| title_auth |
Medical image super-resolution via deep residual neural network in the shearlet domain |
| abstract |
Abstract This paper proposes aconvolutional neural network (CNN)-based efficient medical image super-resolution (SR) method in the shearlet domain. Because of differences between imaging mechanisms optimized for natural images and medical images, the design begins with building a medical image dataset for medical image SR and extracting effective areas to remarkably enhance the training effects of the CNN-based method. Then, a new medical image SR network structure—deep medical super-resolution network (DMSRN)—has been designed in which local residual learning is implemented through a recursive network and combined with global residual learning to heighten the depth of the network on the ground with no parameter increase. This effectively fixes the long-term dependency problem, which causes the prior state layers to barely have any effect on the following state layers. Last, the design addresses the problem of too-smooth reconstruction effects in the CNN-based method in the image space domain; shearlet transform is introduced to DMSRN to restore global topology through low-frequency sub-bands and restore local edge detail information through high-frequency sub-bands. Experimental results show that the proposed method is better than other state-of-the-art methods for medical image SR, which significantly promotes the restoration ability of texture structure and edge details. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstractGer |
Abstract This paper proposes aconvolutional neural network (CNN)-based efficient medical image super-resolution (SR) method in the shearlet domain. Because of differences between imaging mechanisms optimized for natural images and medical images, the design begins with building a medical image dataset for medical image SR and extracting effective areas to remarkably enhance the training effects of the CNN-based method. Then, a new medical image SR network structure—deep medical super-resolution network (DMSRN)—has been designed in which local residual learning is implemented through a recursive network and combined with global residual learning to heighten the depth of the network on the ground with no parameter increase. This effectively fixes the long-term dependency problem, which causes the prior state layers to barely have any effect on the following state layers. Last, the design addresses the problem of too-smooth reconstruction effects in the CNN-based method in the image space domain; shearlet transform is introduced to DMSRN to restore global topology through low-frequency sub-bands and restore local edge detail information through high-frequency sub-bands. Experimental results show that the proposed method is better than other state-of-the-art methods for medical image SR, which significantly promotes the restoration ability of texture structure and edge details. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract This paper proposes aconvolutional neural network (CNN)-based efficient medical image super-resolution (SR) method in the shearlet domain. Because of differences between imaging mechanisms optimized for natural images and medical images, the design begins with building a medical image dataset for medical image SR and extracting effective areas to remarkably enhance the training effects of the CNN-based method. Then, a new medical image SR network structure—deep medical super-resolution network (DMSRN)—has been designed in which local residual learning is implemented through a recursive network and combined with global residual learning to heighten the depth of the network on the ground with no parameter increase. This effectively fixes the long-term dependency problem, which causes the prior state layers to barely have any effect on the following state layers. Last, the design addresses the problem of too-smooth reconstruction effects in the CNN-based method in the image space domain; shearlet transform is introduced to DMSRN to restore global topology through low-frequency sub-bands and restore local edge detail information through high-frequency sub-bands. Experimental results show that the proposed method is better than other state-of-the-art methods for medical image SR, which significantly promotes the restoration ability of texture structure and edge details. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW |
| container_issue |
17 |
| title_short |
Medical image super-resolution via deep residual neural network in the shearlet domain |
| url |
https://doi.org/10.1007/s11042-021-10894-0 |
| remote_bool |
false |
| author2 |
Wang, Simiao Xia, Zhiqiu Li, Qi Ma, Bin Li, Jian Yang, Meihong Shi, Yun-Qing |
| author2Str |
Wang, Simiao Xia, Zhiqiu Li, Qi Ma, Bin Li, Jian Yang, Meihong Shi, Yun-Qing |
| ppnlink |
189064145 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s11042-021-10894-0 |
| up_date |
2024-07-04T07:10:25.990Z |
| _version_ |
1803631487596101632 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2126484629</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505115126.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230505s2021 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11042-021-10894-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2126484629</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11042-021-10894-0-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">070</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wang, Chunpeng</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-3742-5614</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Medical image super-resolution via deep residual neural network in the shearlet domain</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</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">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper proposes aconvolutional neural network (CNN)-based efficient medical image super-resolution (SR) method in the shearlet domain. Because of differences between imaging mechanisms optimized for natural images and medical images, the design begins with building a medical image dataset for medical image SR and extracting effective areas to remarkably enhance the training effects of the CNN-based method. Then, a new medical image SR network structure—deep medical super-resolution network (DMSRN)—has been designed in which local residual learning is implemented through a recursive network and combined with global residual learning to heighten the depth of the network on the ground with no parameter increase. This effectively fixes the long-term dependency problem, which causes the prior state layers to barely have any effect on the following state layers. Last, the design addresses the problem of too-smooth reconstruction effects in the CNN-based method in the image space domain; shearlet transform is introduced to DMSRN to restore global topology through low-frequency sub-bands and restore local edge detail information through high-frequency sub-bands. Experimental results show that the proposed method is better than other state-of-the-art methods for medical image SR, which significantly promotes the restoration ability of texture structure and edge details.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep medical super-resolution network (DMSRN)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Medical image</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Super-resolution</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Shearlet domain</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Simiao</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xia, Zhiqiu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Qi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ma, Bin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Jian</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Meihong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shi, Yun-Qing</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multimedia tools and applications</subfield><subfield code="d">Springer US, 1995</subfield><subfield code="g">80(2021), 17 vom: 06. Mai, Seite 26637-26655</subfield><subfield code="w">(DE-627)189064145</subfield><subfield code="w">(DE-600)1287642-2</subfield><subfield code="w">(DE-576)052842126</subfield><subfield code="x">1380-7501</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:80</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:17</subfield><subfield code="g">day:06</subfield><subfield code="g">month:05</subfield><subfield code="g">pages:26637-26655</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11042-021-10894-0</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-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MKW</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">80</subfield><subfield code="j">2021</subfield><subfield code="e">17</subfield><subfield code="b">06</subfield><subfield code="c">05</subfield><subfield code="h">26637-26655</subfield></datafield></record></collection>
|
| score |
7.3999014 |