Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Transportation in IoT-SDN Using Vertical Handoff Scheme Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

A Perspective Review of Generative Adversarial Network in Medical Image Denoising

verfasst von : S. P. Porkodi, V. Sarada

Erschienen in: Micro-Electronics and Telecommunication Engineering

Verlag: Springer Nature Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This review article discusses using Generative Adversarial Networks (GANs) for image denoising in medical images. GANs have produced optimistic results in enhancing the quality of noisy medical images, which is crucial for accurate diagnosis and treatment. The article provides an overview of the challenges in medical image denoising and the working principle of GANs. The review also summarizes the recent research on using GANs for medical image denoising and compares their performance and significance. Finally, the article discusses the future directions for GAN-based medical image denoising and its potential impact on the healthcare industry.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Pioneering a New Era of Global Transactions: Decentralized Overseas Transactions on the Blockchain
Nächstes Kapitel Osteoporosis Detection Based on X-Ray Using Deep Convolutional Neural Network
Literatur
1.
Sagheer SVM, George SN (2020) A review on medical image denoising algorithms. Biomed Signal Process Control 61:102036CrossRef
2.
Mondal, Maitra M (2014) Denoising and compression of medical image in wavelet 2d. Int J Recent and Innov Trends in Comput Commun 2(2):1–4
3.
Dabov K, Foi A, Katkovnik V, Egiazarian K (2006) Image denoising with block matching and 3d filtering. In: Image processing: algorithms and systems, neural networks, and machine learning, vol 6064. SPIE, pp 354–365
4.
Thakur RS, Chatterjee S, Yadav RN, Gupta L (2023) Medical image denoising using convolutional neural networks. In: Digital image enhancement and reconstruction, Elsevier, pp 115–138
5.
Chai Y, Liu H, Xu J, Samtani S, Jiang Y, Liu H (2023) A multi-label classification with an adversarial-based denoising autoencoder for medical image annotation. ACM Trans Manag Inf Syst 14(2):1–21CrossRef
6.
Gurrola-Ramos J, Dalmau O, Alarc´on TE (2021) A residual dense u-net neural network for image denoising. IEEE Access 9:31742–31754
7.
Fan L, Zhang F, Fan H, Zhang C (2019) Brief review of image denoising techniques. Visual Comput Indus Biomed Art 2(1):1–12
8.
Ben Hamza A, Krim H (2001) Image denoising: a nonlinear robust statistical approach. IEEE Trans Signal Process 49(12):3045–3054
9.
Donoho DL, Johnstone IM (1995) Adapting to unknown smoothness via wavelet shrinkage. J Am Stat Assoc 90(432):1200–1224MathSciNetCrossRef
10.
Alkinani MH, El-Sakka MR (2017) “Patch-based models and algorithms for image denoising: A comparative review between patch-based images denoising methods for additive noise reduction.” EURASIP J Image Video Process 2017(1):1–27
11.
Tian C, Fei L, Zheng W, Xu Y, Zuo W, Lin, C.-W (2020) “Deep learning on image denoising: An overview.” Neural Netw 131:251–275
12.
Dey R, Bhattacharjee D, Nasipuri M (2020) “Image denoising using generative adversarial network.” Intell Comput: Image Process Based Appl, 73–90
13.
Porkodi SP, Sarada V, Maik V, Gurushankar, K, (2022) “Generic image application using gans (generative adversarial networks): A review.” Evol Syst, 1–15
14.
Zhang K, Zuo W, Chen Y, Meng D, Zhang L (2017) “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising.” IEEE Trans Image Process 26(7):3142–3155
15.
Isola P, Zhu J.-Y, Zhou T, Efros AA (2017) “Image-to-image translation with conditional adversarial networks.” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1125–1134
16.
Zhu J-Y, Park T, Isola P, Efros AA (2017) “Unpaired image-to-image trans-lation using cycle-consistent adversarial networks.” In Proceedings of the IEEE international conference on computer vision, pp. 2223–2232
17.
Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC (2017) “Improved training of wasserstein gans.” Adv Neural Inf Process Syst 30
18.
Ledig C, Theis L, Huszár F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z et al (2017) “Photo-realistic single image super-resolution using a generative adversarial network.” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4681–4690
19.
Lample G, Zeghidour N, Usunier N, Bordes A, Denoyer L, Ranzato M (2017) “Fader networks: Manipulating images by sliding attributes.” Adv Neural Inf Process Syst 30
20.
Pascual S, Bonafonte A, Serra J, (2017) “Segan: Speech enhancement generative adversarial network.” arXiv preprint arXiv:1703.09452
21.
Yi Z, Zhang H, Tan P, Gong M (2017) “Dualgan: Unsupervised dual learning for image-to-image translation.” In Proceedings of the IEEE international conference on computer vision, pp. 2849–2857
22.
Choi Y, Choi M, Kim M, Ha J-W, Kim S, Choo J (2018) “Stargan: Unified generative adversarial networks for multi-domain image-to-image translation.” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8789–8797
23.
Huang X, Liu M-Y, Belongie S, Kautz J (2018) “Multimodal unsupervised image-to-image translation.” In Proceedings of the European conference on computer vi-sion (ECCV), pp. 172–189
24.
Zhang H, Goodfellow I, Metaxas D, Odena A (2019) Self-attention generative adversarial networks. In: International conference on machine learning, PMLR, pp 7354–7363
25.
Kim J, Kim M, Kang H, Lee K (2019) U-gat-it: unsupervised generative attentional networks with adaptive layer-instance normalization for image-to-image translation. arXiv preprint arXiv:​1907.​10830
26.
Marcos L, Alirezaie J, Babyn P (2021) Low dose ct image denoising using boosting attention fusion gan with perceptual loss. In: 2021 43rd annual international conference of the IEEE engineering in medicine and biology society (EMBC), IEEE, pp 3407–3410
27.
Zhang L, Zhang J (2022) Ultrasound image denoising using generative adversarial networks with residual dense connectivity and weighted joint loss. PeerJ Comput Sci 8:e873CrossRef
28.
Porkodi SP, Sarada V, Maik V (2023) Dcgan for data augmentation in pneumonia chest x-ray image classification. In: Proceedings of international conference on recent trends in computing: ICRTC 2022, Springer, pp 129–137
Metadaten
Titel
A Perspective Review of Generative Adversarial Network in Medical Image Denoising
verfasst von
S. P. Porkodi
V. Sarada
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Micro-Electronics and Telecommunication Engineering

Print ISBN: 978-981-9995-61-5

Electronic ISBN: 978-981-9995-62-2

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-99-9562-2_15

Neuer Inhalt

    Bildnachweise