Papers › GRDN:Grouped Residual Dense Network for Real Image Denoising and GAN-based Real-world...
GRDN:Grouped Residual Dense Network for Real Image Denoising and GAN-based Real-world Noise Modeling
Dong-Wook Kim, Jae Ryun Chung, Seung-Won Jung
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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Color Image Denoising | NTIRE 2019 Real Image Denoising Challenge (sRGB) | GRDN | PSNR | 39.931743 | #1 of 1 | Archive leaderboard | report |
| Color Image Denoising | NTIRE 2019 Real Image Denoising Challenge (sRGB) | GRDN | SSIM | 0.973589 | #1 of 1 | Archive leaderboard | report |
| Noise Estimation | SIDD | GRDN | Average KL Divergence | 0.443 | #3 of 5 | Archive leaderboard | report |
| Noise Estimation | SIDD | GRDN | PSNR Gap | 2.28 | #3 of 5 | Archive leaderboard | report |
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