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RGB-NIR demosaicing using deep residual U-Net

Ivana Shopovska (UGent) , Ljubomir Jovanov (UGent) and Wilfried Philips (UGent)
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Abstract
Multi-spectral image acquisition brings numerous potential benefits in computer vision and image processing applications. Single-sensor acquisition helps to overcome problems with misalignments occurring in multiple-sensor acquisition. However, the single-sensor approach poses the problem of interpolation of missing values. In this paper we propose an adapted version of a residual U-Net, with application in demosaicing. The experiments show that the proposed method achieves state-of-the-art results, and has good generalization capabilities to different color filter array patterns.
Keywords
CFA, CNN, deep learning, demosaicing, RGB-NIR, single-sensor, U-Net

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Citation

Please use this url to cite or link to this publication:

Chicago
Shopovska, Ivana, Ljubomir Jovanov, and Wilfried Philips. 2018. “RGB-NIR Demosaicing Using Deep Residual U-Net.” In 2018 26TH TELECOMMUNICATIONS FORUM (TELFOR) , 297–300. IEEE.
APA
Shopovska, I., Jovanov, L., & Philips, W. (2018). RGB-NIR demosaicing using deep residual U-Net. 2018 26TH TELECOMMUNICATIONS FORUM (TELFOR) (pp. 297–300). Presented at the 26th Telecommunications Forum (TELFOR), IEEE.
Vancouver
1.
Shopovska I, Jovanov L, Philips W. RGB-NIR demosaicing using deep residual U-Net. 2018 26TH TELECOMMUNICATIONS FORUM (TELFOR) . IEEE; 2018. p. 297–300.
MLA
Shopovska, Ivana, Ljubomir Jovanov, and Wilfried Philips. “RGB-NIR Demosaicing Using Deep Residual U-Net.” 2018 26TH TELECOMMUNICATIONS FORUM (TELFOR) . IEEE, 2018. 297–300. Print.
@inproceedings{8591644,
  abstract     = {Multi-spectral image acquisition brings numerous potential benefits in computer vision and image processing applications. Single-sensor acquisition helps to overcome problems with misalignments occurring in multiple-sensor acquisition. However, the single-sensor approach poses the problem of interpolation of missing values. In this paper we propose an adapted version of a residual U-Net, with application in demosaicing. The experiments show that the proposed method achieves state-of-the-art results, and has good generalization capabilities to different color filter array patterns.},
  articleno    = {18395084},
  author       = {Shopovska, Ivana and Jovanov, Ljubomir and Philips, Wilfried},
  booktitle    = {2018 26TH TELECOMMUNICATIONS FORUM (TELFOR) },
  isbn         = {9781538671719},
  language     = {eng},
  location     = {Belgrade, Serbia},
  pages        = {18395084:297--18395084:300},
  publisher    = {IEEE},
  title        = {RGB-NIR demosaicing using deep residual U-Net},
  url          = {http://dx.doi.org/10.1109/TELFOR.2018.8611819},
  year         = {2018},
}

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