MPIN: a macro-pixel integration network for light field super-resolution
Author:
Publisher
Zhejiang University Press
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing
Link
https://link.springer.com/content/pdf/10.1631/FITEE.2000566.pdf
Reference39 articles.
1. Alain M, Smolic A, 2018. Light field super-resolution via LFBM5D sparse coding. Proc 25th IEEE Int Conf on Image Processing, p.2501–2505. https://doi.org/10.1109/ICIP.2018.8451162
2. Bishop TE, Favaro P, 2012. The light field camera: extended depth of field, aliasing, and superresolution. IEEE Trans Patt Anal Mach Intell, 34(5):972–986. https://doi.org/10.1109/TPAMI.2011.168
3. Bishop TE, Zanetti S, Favaro P, 2009. Light field super-resolution. Proc IEEE Int Conf on Computational Photography, p.1–9. https://doi.org/10.1109/ICCPHOT.2009.5559010
4. Dong C, Loy CC, He KM, et al., 2014. Learning a deep convolutional network for image super-resolution. Proc 13th European Conf on Computer Vision, p.184–199. https://doi.org/10.1007/978-3-319-10593-2_13
5. Fan HZ, Liu D, Xiong ZW, et al., 2017. Two-stage convolutional neural network for light field super-resolution. Proc IEEE Int Conf on Image Processing, p.1167–1171. https://doi.org/10.1109/ICIP.2017.8296465
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