Skip to main content
Erschienen in:

29.12.2023

Low-Dose CT Image Denoising with a Residual Multi-scale Feature Fusion Convolutional Neural Network and Enhanced Perceptual Loss

verfasst von: Farzan Niknejad Mazandarani, Paul Babyn, Javad Alirezaie

Erschienen in: Circuits, Systems, and Signal Processing | Ausgabe 4/2024

Einloggen

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

search-config
loading …

Abstract

Computed tomography (CT) stands as a pivotal medical imaging technique, delivering timely and reliable clinical evaluations. Yet, its dependence on ionizing radiation raises health concerns. One mitigation strategy involves using reduced radiation for low-dose CT (LDCT) imaging; however, this often results in noise artifacts that undermine diagnostic precision. To address this issue, a distinctive CT image denoising technique has been introduced that utilizes deep neural networks to suppress image noise. This advanced CT image denoising network employs an attention mechanism for the feature extraction stage, facilitating the adaptive fusion of multi-scale local characteristics and channel-wide dependencies. Furthermore, a novel residual block has been incorporated, crafted to generate features with superior representational abilities, factoring in diverse spatial scales and eliminating redundant features. A unique loss function is also developed to optimize network parameters, focusing on preserving structural information by capturing high-frequency components and perceptually important details. Experimental results demonstrate the effectiveness of the proposed network in enhancing the quality of LDCT images.

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!

ATZelektronik

Die Fachzeitschrift ATZelektronik bietet für Entwickler und Entscheider in der Automobil- und Zulieferindustrie qualitativ hochwertige und fundierte Informationen aus dem gesamten Spektrum der Pkw- und Nutzfahrzeug-Elektronik. 

Lassen Sie sich jetzt unverbindlich 2 kostenlose Ausgabe zusenden.

ATZelectronics worldwide

ATZlectronics worldwide is up-to-speed on new trends and developments in automotive electronics on a scientific level with a high depth of information. 

Order your 30-days-trial for free and without any commitment.

Weitere Produktempfehlungen anzeigen
Literatur
1.
Zurück zum Zitat A. Abdelhamed, M. Afifi, R. Timofte, M.S. Brown, Ntire 2020 challenge on real image denoising: Dataset, methods and results, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 496–497 (2020) A. Abdelhamed, M. Afifi, R. Timofte, M.S. Brown, Ntire 2020 challenge on real image denoising: Dataset, methods and results, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 496–497 (2020)
2.
Zurück zum Zitat A.A. Abdulla, Efficient computer-aided diagnosis technique for leukaemia cancer detection. IET Image Proc. 14(17), 4435–4440 (2020)CrossRef A.A. Abdulla, Efficient computer-aided diagnosis technique for leukaemia cancer detection. IET Image Proc. 14(17), 4435–4440 (2020)CrossRef
3.
Zurück zum Zitat M.Z. Alom, T.M. Taha, C. Yakopcic, S. Westberg, P. Sidike, M.S. Nasrin, B.C. Van Esesn, A.A.S. Awwal, V.K. Asari, The history began from alexnet: a comprehensive survey on deep learning approaches. arXiv preprint arXiv:1803.01164 (2018) M.Z. Alom, T.M. Taha, C. Yakopcic, S. Westberg, P. Sidike, M.S. Nasrin, B.C. Van Esesn, A.A.S. Awwal, V.K. Asari, The history began from alexnet: a comprehensive survey on deep learning approaches. arXiv preprint arXiv:​1803.​01164 (2018)
4.
Zurück zum Zitat Y. Behroozi, M. Yazdi, A.Z. Asli, Hyperspectral image denoising based on superpixel segmentation low-rank matrix approximation and total variation. Circuits Syst. Signal Process. 41(6), 3372–3396 (2022)CrossRef Y. Behroozi, M. Yazdi, A.Z. Asli, Hyperspectral image denoising based on superpixel segmentation low-rank matrix approximation and total variation. Circuits Syst. Signal Process. 41(6), 3372–3396 (2022)CrossRef
5.
Zurück zum Zitat D.J. Brenner, C.D. Elliston, E.J. Hall, W.E. Berdon et al., Estimated risks of radiation-induced fatal cancer from pediatric ct. Am. J. Roentgenol. 176(2), 289–296 (2001)CrossRef D.J. Brenner, C.D. Elliston, E.J. Hall, W.E. Berdon et al., Estimated risks of radiation-induced fatal cancer from pediatric ct. Am. J. Roentgenol. 176(2), 289–296 (2001)CrossRef
7.
Zurück zum Zitat H. Chen, Y. Zhang, M.K. Kalra, F. Lin, Y. Chen, P. Liao, J. Zhou, G. Wang, Low-dose ct with a residual encoder-decoder convolutional neural network. IEEE Trans. Med. Imaging 36(12), 2524–2535 (2017)CrossRefPubMedPubMedCentral H. Chen, Y. Zhang, M.K. Kalra, F. Lin, Y. Chen, P. Liao, J. Zhou, G. Wang, Low-dose ct with a residual encoder-decoder convolutional neural network. IEEE Trans. Med. Imaging 36(12), 2524–2535 (2017)CrossRefPubMedPubMedCentral
8.
Zurück zum Zitat W. Chen, Y. Shao, Y. Wang, Q. Zhang, Y. Liu, L. Yao, Y. Chen, G. Yang, Z. Gui, A novel total variation model for low-dose ct image denoising. IEEE Access 6, 78892–78903 (2018)CrossRef W. Chen, Y. Shao, Y. Wang, Q. Zhang, Y. Liu, L. Yao, Y. Chen, G. Yang, Z. Gui, A novel total variation model for low-dose ct image denoising. IEEE Access 6, 78892–78903 (2018)CrossRef
9.
Zurück zum Zitat A. Ferrero, N. Takahashi, T.J. Vrtiska, A.E. Krambeck, J.C. Lieske, C.H. McCollough, Understanding, justifying, and optimizing radiation exposure for ct imaging in nephrourology. Nat. Rev. Urol. 16(4), 231–244 (2019)CrossRefPubMedPubMedCentral A. Ferrero, N. Takahashi, T.J. Vrtiska, A.E. Krambeck, J.C. Lieske, C.H. McCollough, Understanding, justifying, and optimizing radiation exposure for ct imaging in nephrourology. Nat. Rev. Urol. 16(4), 231–244 (2019)CrossRefPubMedPubMedCentral
10.
Zurück zum Zitat M.A. Gavrielides, L.M. Kinnard, K.J. Myers, J. Peregoy, W.F. Pritchard, R. Zeng, J. Esparza, J. Karanian, N. Petrick, A resource for the assessment of lung nodule size estimation methods: database of thoracic ct scans of an anthropomorphic phantom. Opt. Express 18(14), 15244–15255 (2010)ADSCrossRefPubMedPubMedCentral M.A. Gavrielides, L.M. Kinnard, K.J. Myers, J. Peregoy, W.F. Pritchard, R. Zeng, J. Esparza, J. Karanian, N. Petrick, A resource for the assessment of lung nodule size estimation methods: database of thoracic ct scans of an anthropomorphic phantom. Opt. Express 18(14), 15244–15255 (2010)ADSCrossRefPubMedPubMedCentral
11.
Zurück zum Zitat M. Gholizadeh-Ansari, J. Alirezaie, P. Babyn, Deep learning for low-dose ct denoising using perceptual loss and edge detection layer. J. Digit. Imaging 33, 504–515 (2020)CrossRefPubMed M. Gholizadeh-Ansari, J. Alirezaie, P. Babyn, Deep learning for low-dose ct denoising using perceptual loss and edge detection layer. J. Digit. Imaging 33, 504–515 (2020)CrossRefPubMed
12.
Zurück zum Zitat I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, A.C. Courville, Improved training of wasserstein gans. Adv. Neural Inf. Process. Syst. 30 (2017) I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, A.C. Courville, Improved training of wasserstein gans. Adv. Neural Inf. Process. Syst. 30 (2017)
13.
Zurück zum Zitat D. Hart, M. Hillier, B. Wall, Doses to patients from medical X-ray examinations in the UK. 2000 review (2002) D. Hart, M. Hillier, B. Wall, Doses to patients from medical X-ray examinations in the UK. 2000 review (2002)
14.
Zurück zum Zitat K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
15.
Zurück zum Zitat J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks
16.
Zurück zum Zitat Y. Huo, D. Wang, Y. Qi, P. Lian, A new gaussian kernel filtering algorithm involving the sparse criterion. Circuits Syst. Signal Process. 42(1), 522–539 (2023)CrossRef Y. Huo, D. Wang, Y. Qi, P. Lian, A new gaussian kernel filtering algorithm involving the sparse criterion. Circuits Syst. Signal Process. 42(1), 522–539 (2023)CrossRef
17.
Zurück zum Zitat L. Jia, A. Huang, X. He, Z. Li, J. Liang, A residual multi-scale feature extraction network with hybrid loss for low-dose computed tomography denoising. Available at SSRN 4327683 L. Jia, A. Huang, X. He, Z. Li, J. Liang, A residual multi-scale feature extraction network with hybrid loss for low-dose computed tomography denoising. Available at SSRN 4327683
19.
Zurück zum Zitat Z. Li, L. Yu, J.D. Trzasko, D.S. Lake, D.J. Blezek, J.G. Fletcher, C.H. McCollough, A. Manduca, Adaptive nonlocal means filtering based on local noise level for ct denoising. Med. Phys. 41(1), 011908 (2014)CrossRefPubMed Z. Li, L. Yu, J.D. Trzasko, D.S. Lake, D.J. Blezek, J.G. Fletcher, C.H. McCollough, A. Manduca, Adaptive nonlocal means filtering based on local noise level for ct denoising. Med. Phys. 41(1), 011908 (2014)CrossRefPubMed
22.
Zurück zum Zitat J. Liu, W. Zhang, Y. Tang, J. Tang, G. Wu, Residual feature aggregation network for image super-resolution J. Liu, W. Zhang, Y. Tang, J. Tang, G. Wu, Residual feature aggregation network for image super-resolution
23.
Zurück zum Zitat Y. Liu, H. Chen, Y. Chen, W. Yin, C. Shen, Generic perceptual loss for modeling structured output dependencies Y. Liu, H. Chen, Y. Chen, W. Yin, C. Shen, Generic perceptual loss for modeling structured output dependencies
24.
Zurück zum Zitat Y. Liu, J. Ma, Y. Fan, Z. Liang, Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction. Phys. Med. Biol. 57(23), 7923 (2012)CrossRefPubMedPubMedCentral Y. Liu, J. Ma, Y. Fan, Z. Liang, Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction. Phys. Med. Biol. 57(23), 7923 (2012)CrossRefPubMedPubMedCentral
25.
Zurück zum Zitat Y. Liu, Z. Gui, Q. Zhang, Noise reduction for low-dose x-ray ct based on fuzzy logical in stationary wavelet domain. Optik-Int. J. Light Electron Opt. 124(18), 3348–3352 (2013)CrossRef Y. Liu, Z. Gui, Q. Zhang, Noise reduction for low-dose x-ray ct based on fuzzy logical in stationary wavelet domain. Optik-Int. J. Light Electron Opt. 124(18), 3348–3352 (2013)CrossRef
26.
Zurück zum Zitat J. Liu, J. Tang, G. Wu, Residual feature distillation network for lightweight image super-resolution, in Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings, Part III 16, pp. 41–55 (2020). Springer J. Liu, J. Tang, G. Wu, Residual feature distillation network for lightweight image super-resolution, in Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings, Part III 16, pp. 41–55 (2020). Springer
27.
Zurück zum Zitat P. Luo, X. Qu, X. Qing, J. Gu, Ct image denoising using double density dual tree complex wavelet with modified thresholding, in 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA), pp. 287–290 (2018). IEEE P. Luo, X. Qu, X. Qing, J. Gu, Ct image denoising using double density dual tree complex wavelet with modified thresholding, in 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA), pp. 287–290 (2018). IEEE
28.
Zurück zum Zitat J. Ma, J. Huang, Q. Feng, H. Zhang, H. Lu, Z. Liang, W. Chen, Low-dose computed tomography image restoration using previous normal-dose scan. Med. Phys. 38(10), 5713–5731 (2011)CrossRefPubMedPubMedCentral J. Ma, J. Huang, Q. Feng, H. Zhang, H. Lu, Z. Liang, W. Chen, Low-dose computed tomography image restoration using previous normal-dose scan. Med. Phys. 38(10), 5713–5731 (2011)CrossRefPubMedPubMedCentral
29.
Zurück zum Zitat L. Ma, H. Xue, G. Yang, Z. Zhang, C. Li, Y. Yao, Y. Teng, Scrdn: Residual dense network with self-calibrated convolutions for low dose ct image denoising. Nucl. Inst. Methods Phys. Res. 1045, 167625 (2023)CrossRef L. Ma, H. Xue, G. Yang, Z. Zhang, C. Li, Y. Yao, Y. Teng, Scrdn: Residual dense network with self-calibrated convolutions for low dose ct image denoising. Nucl. Inst. Methods Phys. Res. 1045, 167625 (2023)CrossRef
30.
Zurück zum Zitat A. Manduca, L. Yu, J.D. Trzasko, N. Khaylova, J.M. Kofler, C.M. McCollough, J.G. Fletcher, Projection space denoising with bilateral filtering and ct noise modeling for dose reduction in ct. Med. Phys. 36(11), 4911–4919 (2009)CrossRefPubMedPubMedCentral A. Manduca, L. Yu, J.D. Trzasko, N. Khaylova, J.M. Kofler, C.M. McCollough, J.G. Fletcher, Projection space denoising with bilateral filtering and ct noise modeling for dose reduction in ct. Med. Phys. 36(11), 4911–4919 (2009)CrossRefPubMedPubMedCentral
34.
Zurück zum Zitat P.A. Oakley, D.E. Harrison, Death of the alara radiation protection principle as used in the medical sector. Dose-Response 18(2), 1559325820921641 (2020)CrossRefPubMedPubMedCentral P.A. Oakley, D.E. Harrison, Death of the alara radiation protection principle as used in the medical sector. Dose-Response 18(2), 1559325820921641 (2020)CrossRefPubMedPubMedCentral
35.
Zurück zum Zitat Y. Pathak, K. Arya, S. Tiwari, Low-dose ct image reconstruction using gain intervention-based dictionary learning. Mod. Phys. Lett. B 32(14), 1850148 (2018)ADSMathSciNetCrossRef Y. Pathak, K. Arya, S. Tiwari, Low-dose ct image reconstruction using gain intervention-based dictionary learning. Mod. Phys. Lett. B 32(14), 1850148 (2018)ADSMathSciNetCrossRef
36.
Zurück zum Zitat K. Rao, M. Bansal, G. Kaur, An effective ct medical image enhancement system based on dt-cwt and adaptable morphology. Circuits Syst. Signal Process. 42(2), 1034–1062 (2023)CrossRef K. Rao, M. Bansal, G. Kaur, An effective ct medical image enhancement system based on dt-cwt and adaptable morphology. Circuits Syst. Signal Process. 42(2), 1034–1062 (2023)CrossRef
37.
Zurück zum Zitat D.S. Rigie, A.A. Sanchez, P.J. La Rivière, Assessment of vectorial total variation penalties on realistic dual-energy ct data. Phys. Med. Biol. 62(8), 3284 (2017)CrossRefPubMedPubMedCentral D.S. Rigie, A.A. Sanchez, P.J. La Rivière, Assessment of vectorial total variation penalties on realistic dual-energy ct data. Phys. Med. Biol. 62(8), 3284 (2017)CrossRefPubMedPubMedCentral
38.
Zurück zum Zitat K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, in 3rd International Conference on Learning Representations, ICLR 2015—Conference Track Proceedings, pp. 1–14 (2015) K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, in 3rd International Conference on Learning Representations, ICLR 2015—Conference Track Proceedings, pp. 1–14 (2015)
39.
Zurück zum Zitat M. Su, J. Zheng, Y. Yang, Q. Wu, A new multipath mitigation method based on adaptive thresholding wavelet denoising and double reference shift strategy. GPS Sol. 22, 1–12 (2018)CrossRef M. Su, J. Zheng, Y. Yang, Q. Wu, A new multipath mitigation method based on adaptive thresholding wavelet denoising and double reference shift strategy. GPS Sol. 22, 1–12 (2018)CrossRef
40.
Zurück zum Zitat Z. Tian, X. Jia, K. Yuan, T. Pan, S.B. Jiang, Low-dose ct reconstruction via edge-preserving total variation regularization. Phys. Med. Biol. 56(18), 5949 (2011)CrossRefPubMedPubMedCentral Z. Tian, X. Jia, K. Yuan, T. Pan, S.B. Jiang, Low-dose ct reconstruction via edge-preserving total variation regularization. Phys. Med. Biol. 56(18), 5949 (2011)CrossRefPubMedPubMedCentral
41.
Zurück zum Zitat P. Wang, Y. Li, A. Research, N. Vasconcelos, S. Diego, Rethinking and improving the robustness of image style transfer P. Wang, Y. Li, A. Research, N. Vasconcelos, S. Diego, Rethinking and improving the robustness of image style transfer
42.
Zurück zum Zitat J. Wang, H. Lu, T. Li, Z. Liang, Sinogram noise reduction for low-dose ct by statistics-based nonlinear filters, in Medical Imaging 2005: Image Processing, vol. 5747, pp. 2058–2066 (2005). SPIE J. Wang, H. Lu, T. Li, Z. Liang, Sinogram noise reduction for low-dose ct by statistics-based nonlinear filters, in Medical Imaging 2005: Image Processing, vol. 5747, pp. 2058–2066 (2005). SPIE
44.
Zurück zum Zitat Q. Yang, P. Yan, S. Member, Y. Zhang, Low-dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging 37, 1348–1357 (2018)CrossRefPubMedPubMedCentral Q. Yang, P. Yan, S. Member, Y. Zhang, Low-dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging 37, 1348–1357 (2018)CrossRefPubMedPubMedCentral
45.
46.
Zurück zum Zitat Y. Zhang, J. Zhang, H. Lu, Statistical sinogram smoothing for low-dose ct with segmentation-based adaptive filtering. IEEE Trans. Nucl. Sci. 57(5), 2587–2598 (2010)ADSCrossRef Y. Zhang, J. Zhang, H. Lu, Statistical sinogram smoothing for low-dose ct with segmentation-based adaptive filtering. IEEE Trans. Nucl. Sci. 57(5), 2587–2598 (2010)ADSCrossRef
48.
Zurück zum Zitat Y.-D. Zhang, Z. Zhang, X. Zhang, S.-H. Wang, Midcan: a multiple input deep convolutional attention network for covid-19 diagnosis based on chest ct and chest X-ray. Pattern Recogn. Lett. 150, 8–16 (2021)ADSCrossRef Y.-D. Zhang, Z. Zhang, X. Zhang, S.-H. Wang, Midcan: a multiple input deep convolutional attention network for covid-19 diagnosis based on chest ct and chest X-ray. Pattern Recogn. Lett. 150, 8–16 (2021)ADSCrossRef
49.
Zurück zum Zitat J. Zhang, J. Lv, Y. Cheng, A novel denoising method for medical ct images based on moving decomposition framework. Circuits Syst. Signal Process. 41(12), 6885–6905 (2022)CrossRef J. Zhang, J. Lv, Y. Cheng, A novel denoising method for medical ct images based on moving decomposition framework. Circuits Syst. Signal Process. 41(12), 6885–6905 (2022)CrossRef
50.
Zurück zum Zitat M. Zhang, S. Gu, Y. Shi, The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review. Compl. Intel. Syst. 8(6), 5545–5561 (2022)CrossRef M. Zhang, S. Gu, Y. Shi, The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review. Compl. Intel. Syst. 8(6), 5545–5561 (2022)CrossRef
52.
Zurück zum Zitat P. Zhang, Y. Liu, Z. Gui, Y. Chen, L. Jia, A region-adaptive non-local denoising algorithm for low-dose computed tomography images. Math. Biosci. Eng. 20(2), 2831–2846 (2023)CrossRefPubMed P. Zhang, Y. Liu, Z. Gui, Y. Chen, L. Jia, A region-adaptive non-local denoising algorithm for low-dose computed tomography images. Math. Biosci. Eng. 20(2), 2831–2846 (2023)CrossRefPubMed
53.
Zurück zum Zitat T. Zhang, D. Wu, X. Mo, The rank residual constraint model with weighted schatten p-norm minimization for image denoising. Circuits Syst. Signal Process. pp. 1–19 (2023) T. Zhang, D. Wu, X. Mo, The rank residual constraint model with weighted schatten p-norm minimization for image denoising. Circuits Syst. Signal Process. pp. 1–19 (2023)
54.
55.
Zurück zum Zitat W. Zhao, H. Lu, Medical image fusion and denoising with alternating sequential filter and adaptive fractional order total variation. IEEE Trans. Instrum. Meas. 66(9), 2283–2294 (2017)ADSCrossRef W. Zhao, H. Lu, Medical image fusion and denoising with alternating sequential filter and adaptive fractional order total variation. IEEE Trans. Instrum. Meas. 66(9), 2283–2294 (2017)ADSCrossRef
56.
Zurück zum Zitat T. Zhao, M. McNitt-Gray, D. Ruan, A convolutional neural network for ultra-low-dose ct denoising and emphysema screening. Med. Phys. 46(9), 3941–3950 (2019)CrossRefPubMed T. Zhao, M. McNitt-Gray, D. Ruan, A convolutional neural network for ultra-low-dose ct denoising and emphysema screening. Med. Phys. 46(9), 3941–3950 (2019)CrossRefPubMed
57.
Zurück zum Zitat F. Zhao, M. Liu, Z. Gao, X. Jiang, R. Wang, L. Zhang, Dual-scale similarity-guided cycle generative adversarial network for unsupervised low-dose ct denoising. Comput. Biol. Med. 161, 107029 (2023)CrossRefPubMed F. Zhao, M. Liu, Z. Gao, X. Jiang, R. Wang, L. Zhang, Dual-scale similarity-guided cycle generative adversarial network for unsupervised low-dose ct denoising. Comput. Biol. Med. 161, 107029 (2023)CrossRefPubMed
Metadaten
Titel
Low-Dose CT Image Denoising with a Residual Multi-scale Feature Fusion Convolutional Neural Network and Enhanced Perceptual Loss
verfasst von
Farzan Niknejad Mazandarani
Paul Babyn
Javad Alirezaie
Publikationsdatum
29.12.2023
Verlag
Springer US
Erschienen in
Circuits, Systems, and Signal Processing / Ausgabe 4/2024
Print ISSN: 0278-081X
Elektronische ISSN: 1531-5878
DOI
https://doi.org/10.1007/s00034-023-02575-0