Abstract
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.
Abstract (translated)
近年来，随着深度学习体系结构，特别是卷积神经网络的发展，图像去噪的研究也取得了很大进展。然而，实际图像去噪仍然具有很大的挑战性，因为无法获得理想的地面真值图像对和实际噪声图像对。由于最近发布了基准数据集，图像去噪社区的兴趣现在正朝着现实世界的去噪问题发展。本文提出了一种分组剩余密度网络（GRDN），它是最新的剩余密度网络（RDN）的扩展和广义结构。RDN的核心部分被定义为分组剩余密度块（GRDB），用作GRDN的构建模块。实验表明，级联GRDBS可以显著提高图像去噪性能。除了网络体系结构的设计外，我们还开发了一种新的基于生成对抗网络的现实世界噪声建模方法。我们通过在NTIRE2019真实图像去噪挑战中达到最高的峰值信噪比和结构相似性来证明所提出的方法的优越性——第2道：SRGB。
URL
https://arxiv.org/abs/1905.11172
