@inproceedings{909c791aea2646b8a59586e84f8b4558,
title = "An ELU Network with Total Variation for Image Denoising",
abstract = "In this paper, we propose a novel convolutional neural network (CNN) for image denoising, which uses exponential linear unit (ELU) as the activation function. We investigate the suitability by analyzing ELU{\textquoteright}s connection with trainable nonlinear reaction diffusion model (TNRD) and residual denoising. On the other hand, batch normalization (BN) is indispensable for residual denoising and convergence purpose. However, direct stacking of BN and ELU degrades the performance of CNN. To mitigate this issue, we design an innovative combination of activation layer and normalization layer to exploit and leverage the ELU network, and discuss the corresponding rationale. Moreover, inspired by the fact that minimizing total variation (TV) can be applied to image denoising, we propose a TV regularized L2 loss to evaluate the training effect during the iterations. Finally, we conduct extensive experiments, showing that our model outperforms some recent and popular approaches on Gaussian denoising with specific or randomized noise levels for both gray and color images.",
keywords = "Convolutional neural network, Deep learning, ELU, Image denoising, Image processing, Total variation",
author = "Tianyang Wang and Zhengrui Qin and Michelle Zhu",
year = "2017",
month = jan,
day = "1",
doi = "10.1007/978-3-319-70090-8_24",
language = "English",
isbn = "9783319700892",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "227--237",
editor = "Derong Liu and Shengli Xie and El-Alfy, {El-Sayed M.} and Dongbin Zhao and Yuanqing Li",
booktitle = "Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings",
note = "24th International Conference on Neural Information Processing, ICONIP 2017 ; Conference date: 14-11-2017 Through 18-11-2017",
}