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

arXiv: https://arxiv.org/abs/1905.11172 · HTML: https://mlsanity.com/paper/1905.11172/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2019.

## Built on by (most cited first, 30 of 33)

- [R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network](https://mlsanity.com/paper/2106.14501/) (2021)
- [Invertible Denoising Network: A Light Solution for Real Noise Removal](https://mlsanity.com/paper/2104.10546/) (2021)
- [Rethinking Noise Synthesis and Modeling in Raw Denoising](https://mlsanity.com/paper/2110.04756/) (2021)
- [C2N: Practical Generative Noise Modeling for Real-World Denoising](https://mlsanity.com/paper/2202.09533/) (2022)
- [Contextformer: A Transformer with Spatio-Channel Attention for Context Modeling in Learned Image Compression](https://mlsanity.com/paper/2203.02452/) (2022)
- [Towards General Low-Light Raw Noise Synthesis and Modeling](https://mlsanity.com/paper/2307.16508/) (2023)
- [Learn from Unpaired Data for Image Restoration: A Variational Bayes Approach](https://mlsanity.com/paper/2204.10090/) (2022)
- [FlexHDR: Modelling Alignment and Exposure Uncertainties for Flexible HDR Imaging](https://mlsanity.com/paper/2201.02625/) (2022)
- [Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Images](https://mlsanity.com/paper/2206.01103/) (2022)
- [Progressive Joint Low-light Enhancement and Noise Removal for Raw Images](https://mlsanity.com/paper/2106.14844/) (2021)
- [Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation](https://mlsanity.com/paper/2101.02121/) (2021)
- [RCRN: Real-world Character Image Restoration Network via Skeleton Extraction](https://mlsanity.com/paper/2207.07795/) (2022)
- [Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image Denoiser](https://mlsanity.com/paper/2103.10234/) (2021)
- [Modular Degradation Simulation and Restoration for Under-Display Camera](https://mlsanity.com/paper/2209.11455/) (2022)
- [Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoising](https://mlsanity.com/paper/2208.11803/) (2022)
- [Realistic Noise Synthesis with Diffusion Models](https://mlsanity.com/paper/2305.14022/) (2023)
- [Heterogeneous window transformer for image denoising](https://mlsanity.com/paper/2407.05709/) (2024)
- [DeepHQ: Learned Hierarchical Quantizer for Progressive Deep Image Coding](https://mlsanity.com/paper/2408.12150/) (2024)
- [DFPN: Deformable Frame Prediction Network](https://mlsanity.com/paper/2105.12794/) (2021)
- [Robust Deep Ensemble Method for Real-world Image Denoising](https://mlsanity.com/paper/2206.03691/) (2022)
- [Generalized Product-of-Experts for Learning Multimodal Representations in Noisy Environments](https://mlsanity.com/paper/2211.03587/) (2022)
- [NoiseTransfer: Image Noise Generation with Contrastive Embeddings](https://mlsanity.com/paper/2301.13554/) (2023)
- [Self-supervised HDR Imaging from Motion and Exposure Cues](https://mlsanity.com/paper/2203.12311/) (2022)
- [Self-supervision versus synthetic datasets: which is the lesser evil in the context of video denoising?](https://mlsanity.com/paper/2204.11493/) (2022)
- [Test-time Adaptation for Real Image Denoising via Meta-transfer Learning](https://mlsanity.com/paper/2207.02066/) (2022)
- [Convolutional versus Self-Organized Operational Neural Networks for Real-World Blind Image Denoising](https://mlsanity.com/paper/2103.03070/) (2021)
- [Contrastive Monotonic Pixel-Level Modulation](https://mlsanity.com/paper/2207.11517/) (2022)
- [DnSwin: Toward Real-World Denoising via Continuous Wavelet Sliding-Transformer](https://mlsanity.com/paper/2207.13861/) (2022)
- [Deep Variation Prior: Joint Image Denoising and Noise Variance Estimation without Clean Data](https://mlsanity.com/paper/2209.09214/) (2022)
- [A Sub-band Approach to Deep Denoising Wavelet Networks and a Frequency-adaptive Loss for Perceptual Quality](https://mlsanity.com/paper/2102.07973/) (2021)

## Abstract (arXiv)

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.