  Recent research on image denoising has progressed with the development of
deep learning architectures, especially convolutional neural networks. However,
real-world image denoising is still very challenging because it is not possible
to obtain ideal pairs of ground-truth images and real-world noisy images. Owing
to the recent release of benchmark datasets, the interest of the image
denoising community is now moving toward the real-world denoising problem. In
this paper, we propose a grouped residual dense network (GRDN), which is an
extended and generalized architecture of the state-of-the-art residual dense
network (RDN). The core part of RDN is defined as grouped residual dense block
(GRDB) and used as a building module of GRDN. We experimentally show that the
image denoising performance can be significantly improved by cascading GRDBs.
In addition to the network architecture design, we also develop a new
generative adversarial network-based real-world noise modeling method. We
demonstrate the superiority of the proposed methods by achieving the highest
score in terms of both the peak signal-to-noise ratio and the structural
similarity in the NTIRE2019 Real Image Denoising Challenge - Track 2:sRGB.

Image and Video Processing;Computer Vision and Pattern Recognition