  The development of neural networks has greatly improved the performance in
various computer vision tasks. In the filed of image denoising, convolutional
neural network based methods such as DnCNN break through the limits of
classical methods, achieving better quantitative results. However, the
epistemic uncertainty existing in neural networks limits further improvements
in their performance over denoising tasks. Therefore, we develop and study
different solutions to minimize uncertainty and further improve the removal of
noise. From the perspective of ensemble learning, we implement manipulations to
noisy images from the point of view of spatial and frequency domains and then
denoise them using pre-trained denoising networks. We propose a fusion model
consisting of two attention modules, which focus on assigning the proper
weights to pixels and channels. The experimental results show that our model
achieves better performance on top of the baseline of regular pre-trained
denoising networks.
