Abstract
Deep convolutional neural networks perform better on images containing spatially invariant noise (synthetic noise); however, their performance is limited on real-noisy photographs and requires multiple stage network modeling. To advance the practicability of denoising algorithms, this paper proposes a novel single-stage blind real image denoising network (RIDNet) by employing a modular architecture. We use a residual on the residual structure to ease the flow of low-frequency information and apply feature attention to exploit the channel dependencies. Furthermore, the evaluation in terms of quantitative metrics and visual quality on three synthetic and four real noisy datasets against 19 state-of-the-art algorithms demonstrate the superiority of our RIDNet.
Abstract (translated)
深卷积神经网络在含有空间不变噪声（合成噪声）的图像上表现较好，但其性能受到真实噪声照片的限制，需要进行多级网络建模。为了提高去噪算法的实用性，提出了一种采用模块化结构的单级盲实图像去噪网络。我们利用残差结构来缓解低频信息的流动，并利用特征关注来开发信道依赖性。此外，通过对三个合成和四个真实噪声数据集的量化指标和视觉质量的评估，对比19种最先进的算法，证明了RIDNet的优越性。
URL
https://arxiv.org/abs/1904.07396
