This letter presents a convolutional neural network (CNN) for image denoising, especially for the reduction of real noises. As a network topology, we adopt the dual path network (DPN) that combines the advantages of residual and densely connected networks. Using the DPN as a basic building block, we design a network that connects the DPN in dual path again with an attention mechanism. For efficient denoising of real noise images, we build a training set where noisy images are obtained from a heteroscedastic Gaussian noise model and in-camera pipeline. In addition, we augment the synthetic training set with a relatively small number of real noise data. In the experiments, the proposed method is shown to provide state-of-the-art performance in reducing both synthetic and real noises.

中文翻译：

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真实照片噪声的双路径去噪网络

这封信提出了一种用于图像去噪的卷积神经网络 (CNN)，特别是用于减少真实噪声。作为网络拓扑，我们采用了双路径网络（DPN），它结合了残差网络和密集连接网络的优点。使用 DPN 作为基本构建块，我们设计了一个网络，通过注意机制再次以双路径连接 DPN。为了对真实噪声图像进行有效去噪，我们构建了一个训练集，其中噪声图像是从异方差高斯噪声模型和相机内管道中获得的。此外，我们使用相对较少的真实噪声数据来增强合成训练集。在实验中，所提出的方法被证明在减少合成和真实噪声方面提供了最先进的性能。