  Recently, convolutional neural networks (CNNs) have been widely used in image
denoising. Existing methods benefited from residual learning and achieved high
performance. Much research has been paid attention to optimizing the network
architecture of CNN but ignored the limitations of residual learning. This
paper suggests two limitations of it. One is that residual learning focuses on
estimating noise, thus overlooking the image information. The other is that the
image self-similarity is not effectively considered. This paper proposes a
compositional denoising network (CDN), whose image information path (IIP) and
noise estimation path (NEP) will solve the two problems, respectively. IIP is
trained by an image-to-image way to extract image information. For NEP, it
utilizes the image self-similarity from the perspective of training. This
similarity-based training method constrains NEP to output a similar estimated
noise distribution for different image patches with a specific kind of noise.
Finally, image information and noise distribution information will be
comprehensively considered for image denoising. Experiments show that CDN
achieves state-of-the-art results in synthetic and real-world image denoising.
Our code will be released on https://github.com/JiaHongZ/CDN.
