End-to-End Exposure Fusion Using Convolutional Neural Network
Author:
Affiliation:
1. School of Computer and Control Engineering, University of Chinese Academy of Sciences
2. College of Information Technology, Beijing Union University
3. Beijing Key Laboratory of Information Service Engineering
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Subject
Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software
Link
https://www.jstage.jst.go.jp/article/transinf/E101.D/2/E101.D_2017EDL8173/_pdf
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3. [3] Y. Liu, X. Chen, H. Peng, and Z. Wang, “Multi-focus image fusion with a deep convolutional neural network,” Information Fusion, vol.36, pp.191-207, 2017. 10.1016/j.inffus.2016.12.001
4. [4] T. Mertens, J. Kautz, and F.V. Reeth, “Exposure fusion,” Proc. Pacific Graphics, pp.382-390, Maui, Hawaii, 2007. 10.1109/pg.2007.17
5. [5] A.A. Goshtasby, “Fusion of multi-exposure images,” Image and Vision Computing, vol.23, no.6, pp.611-618, 2005. 10.1016/j.imavis.2005.02.004
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