  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'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.
