Filter-Flow-Based Bit-Depth Expansion
Author:
Affiliation:
1. Graduate School of Engineering, Tamagawa University
2. Department of Electrical and Electronic Engineering, Chukyo University
Publisher
Research Institute of Signal Processing, Japan
Subject
General Energy
Link
https://www.jstage.jst.go.jp/article/jsp/26/4/26_115/_pdf
Reference9 articles.
1. [1] S. Kong and C. Fowlkes: Image reconstruction with predictive filter flow, arXiv preprint arXiv:1811.11482v1, 2018.
2. [2] J. Byun, K. Shim and C. Kim: BitNet: Learning-based bit-depth expansion, arXiv preprint arXiv:1910.04397v1, 2019.
3. [3] J. Liu, W. Sun, Y. Su, P. Jing and X. Yang: BE-CALF: Bit-depth enhancement by concatenating all level features of DNN, IEEE Trans. on Image Processing, Vol. 28, No. 10, pp. 4926–4940, 2019.
4. [4] S. M. Seitz and S. Baker: Filter flow, Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 143–150, 2009.
5. [5] D. Kingma and J. Ba: Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980v9, 2017.
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