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Recent research on image denoising has progressed with the development ofdeep learning architectures, especially convolutional neural networks. However,real-world image denoising is still very challenging because it is not possibleto obtain ideal pairs of ground-truth images and real-world noisy images. Owingto the recent release of benchmark datasets, the interest of the imagedenoising community is now moving toward the real-world denoising problem. Inthis paper, we propose a grouped residual dense network (GRDN), which is anextended and generalized architecture of the state-of-the-art residual densenetwork (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 theimage denoising performance can be significantly improved by cascading GRDBs.In addition to the network architecture design, we also develop a newgenerative adversarial network-based real-world noise modeling method. Wedemonstrate the superiority of the proposed methods by achieving the highestscore in terms of both the peak signal-to-noise ratio and the structuralsimilarity in the NTIRE2019 Real Image Denoising Challenge - Track 2:sRGB.

GRDN:Grouped Residual Dense Network for Real Image Denoising and GAN-based Real-world Noise Modeling