  Deep learning based methods have achieved the state-of-the-art performance in
image denoising. In this paper, a deep learning based denoising method is
proposed and a module called fusion block is introduced in the convolutional
neural network. For this so-called Noise Fusion Convolutional Neural Network
(NFCNN), there are two branches in its multi-stage architecture. One branch
aims to predict the latent clean image, while the other one predicts the
residual image. A fusion block is contained between every two stages by taking
the predicted clean image and the predicted residual image as a part of inputs,
and it outputs a fused result to the next stage. NFCNN has an attractive
texture preserving ability because of the fusion block. To train NFCNN, a
stage-wise supervised training strategy is adopted to avoid the vanishing
gradient and exploding gradient problems. Experimental results show that NFCNN
is able to perform competitive denoising results when compared with some
state-of-the-art algorithms.
