Modified convolutional neural network with pseudo-CNN for removing nonlinear noise in digital images
• This article proposes a two stage denoising model for removing Impulse noise and Gaussian-Impulse noise from digital images using P-CNN and modified CNN. • The novel Pseudo-CNN in the preprocessing stage improves the performance of denoising by retrieving salient image features from noisy image. •...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Paul, Eldho [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Les rhinites allergiques d’origine professionnelle - Dallagi, A. ELSEVIER, 2023, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:74 ; year:2022 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.displa.2022.102258 |
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ELV059108819 |
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| 520 | |a • This article proposes a two stage denoising model for removing Impulse noise and Gaussian-Impulse noise from digital images using P-CNN and modified CNN. • The novel Pseudo-CNN in the preprocessing stage improves the performance of denoising by retrieving salient image features from noisy image. • Furthermore, a modified Convolutional Neural Network is designed to filter the residual noise in the preprocessed image. • To improve the network performance, we have used batch normalization and residual strategy in the modified CNN. | ||
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| 650 | 7 | |a Random Valued Impulse Noise (RVIN) |2 Elsevier | |
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10.1016/j.displa.2022.102258 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001920.pica (DE-627)ELV059108819 (ELSEVIER)S0141-9382(22)00081-6 DE-627 ger DE-627 rakwb eng 610 VZ Paul, Eldho verfasserin aut Modified convolutional neural network with pseudo-CNN for removing nonlinear noise in digital images 2022 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • This article proposes a two stage denoising model for removing Impulse noise and Gaussian-Impulse noise from digital images using P-CNN and modified CNN. • The novel Pseudo-CNN in the preprocessing stage improves the performance of denoising by retrieving salient image features from noisy image. • Furthermore, a modified Convolutional Neural Network is designed to filter the residual noise in the preprocessed image. • To improve the network performance, we have used batch normalization and residual strategy in the modified CNN. Gaussian-Impulse Noise Elsevier Nonlinear filtering Elsevier Salt and Pepper Noise (SPN) Elsevier Impulse Noise Elsevier Random Valued Impulse Noise (RVIN) Elsevier Convolutional Neural Network (CNN) Elsevier R.S., Sabeenian oth Enthalten in Elsevier Science Dallagi, A. ELSEVIER Les rhinites allergiques d’origine professionnelle 2023 Amsterdam [u.a.] (DE-627)ELV009519076 volume:74 year:2022 pages:0 https://doi.org/10.1016/j.displa.2022.102258 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 74 2022 0 |
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10.1016/j.displa.2022.102258 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001920.pica (DE-627)ELV059108819 (ELSEVIER)S0141-9382(22)00081-6 DE-627 ger DE-627 rakwb eng 610 VZ Paul, Eldho verfasserin aut Modified convolutional neural network with pseudo-CNN for removing nonlinear noise in digital images 2022 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • This article proposes a two stage denoising model for removing Impulse noise and Gaussian-Impulse noise from digital images using P-CNN and modified CNN. • The novel Pseudo-CNN in the preprocessing stage improves the performance of denoising by retrieving salient image features from noisy image. • Furthermore, a modified Convolutional Neural Network is designed to filter the residual noise in the preprocessed image. • To improve the network performance, we have used batch normalization and residual strategy in the modified CNN. Gaussian-Impulse Noise Elsevier Nonlinear filtering Elsevier Salt and Pepper Noise (SPN) Elsevier Impulse Noise Elsevier Random Valued Impulse Noise (RVIN) Elsevier Convolutional Neural Network (CNN) Elsevier R.S., Sabeenian oth Enthalten in Elsevier Science Dallagi, A. ELSEVIER Les rhinites allergiques d’origine professionnelle 2023 Amsterdam [u.a.] (DE-627)ELV009519076 volume:74 year:2022 pages:0 https://doi.org/10.1016/j.displa.2022.102258 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 74 2022 0 |
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• This article proposes a two stage denoising model for removing Impulse noise and Gaussian-Impulse noise from digital images using P-CNN and modified CNN. • The novel Pseudo-CNN in the preprocessing stage improves the performance of denoising by retrieving salient image features from noisy image. • Furthermore, a modified Convolutional Neural Network is designed to filter the residual noise in the preprocessed image. • To improve the network performance, we have used batch normalization and residual strategy in the modified CNN. |
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• This article proposes a two stage denoising model for removing Impulse noise and Gaussian-Impulse noise from digital images using P-CNN and modified CNN. • The novel Pseudo-CNN in the preprocessing stage improves the performance of denoising by retrieving salient image features from noisy image. • Furthermore, a modified Convolutional Neural Network is designed to filter the residual noise in the preprocessed image. • To improve the network performance, we have used batch normalization and residual strategy in the modified CNN. |
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• This article proposes a two stage denoising model for removing Impulse noise and Gaussian-Impulse noise from digital images using P-CNN and modified CNN. • The novel Pseudo-CNN in the preprocessing stage improves the performance of denoising by retrieving salient image features from noisy image. • Furthermore, a modified Convolutional Neural Network is designed to filter the residual noise in the preprocessed image. • To improve the network performance, we have used batch normalization and residual strategy in the modified CNN. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV059108819</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625012101.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221103s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.displa.2022.102258</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001920.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV059108819</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0141-9382(22)00081-6</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">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Paul, Eldho</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Modified convolutional neural network with pseudo-CNN for removing nonlinear noise in digital images</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• This article proposes a two stage denoising model for removing Impulse noise and Gaussian-Impulse noise from digital images using P-CNN and modified CNN. • The novel Pseudo-CNN in the preprocessing stage improves the performance of denoising by retrieving salient image features from noisy image. • Furthermore, a modified Convolutional Neural Network is designed to filter the residual noise in the preprocessed image. • To improve the network performance, we have used batch normalization and residual strategy in the modified CNN.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Gaussian-Impulse Noise</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Nonlinear filtering</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Salt and Pepper Noise (SPN)</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Impulse Noise</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Random Valued Impulse Noise (RVIN)</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Convolutional Neural Network (CNN)</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">R.S., Sabeenian</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Dallagi, A. 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