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 multistage 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.

基于深度学习的方法在图像去噪方面取得了最先进的性能。本文提出了一种基于深度学习的去噪方法，并在卷积神经网络中引入了一个称为融合块的模块。对于这种所谓的噪声融合卷积神经网络（NFCNN），其多级架构中有两个分支。一个分支旨在预测潜在的干净图像，而另一个分支则预测残差图像。将预测的干净图像和预测的残差图像作为输入的一部分，每两个阶段之间包含一个融合块，并将融合结果输出到下一阶段。由于融合块，NFCNN 具有有吸引力的纹理保留能力。为了训练 NFCNN，采用分阶段监督训练策略来避免梯度消失和梯度爆炸问题。实验结果表明，与一些最先进的算法相比，NFCNN 能够执行有竞争力的去噪结果。