In recent years, deep convolutional neural networks have shown fascinating performance in the field of image denoising. However, deeper network architectures are often accompanied with large numbers of model parameters, leading to high training cost and long inference time, which limits their application in practical denoising tasks. In this paper, we propose a novel dual convolutional blind denoising network with skip connection (DCBDNet), which is able to achieve a desirable balance between the denoising effect and network complexity. The proposed DCBDNet consists of a noise estimation network and a dual convolutional neural network (CNN). The noise estimation network is used to estimate the noise level map, which improves the flexibility of the proposed model. The dual CNN contains two branches: a u-shaped sub-network is designed for the upper branch, and the lower branch is composed of the dilated convolution layers. Skip connections between layers are utilized in both the upper and lower branches. The proposed DCBDNet was evaluated on several synthetic and real-world image denoising benchmark datasets. Experimental results have demonstrated that the proposed DCBDNet can effectively remove gaussian noise in a wide range of levels, spatially variant noise and real noise. With a simple model structure, our proposed DCBDNet still can obtain competitive denoising performance compared to the state-of-the-art image denoising models containing complex architectures. Namely, a favorable trade-off between denoising performance and model complexity is achieved. Codes are available at https://github.com/WenCongWu/DCBDNet.

翻译：近年来，深度卷积神经网络在图像去噪领域展现出令人瞩目的性能。然而，更深层的网络架构往往伴随大量模型参数，导致训练成本高、推理时间长，限制了其在实际去噪任务中的应用。本文提出一种新颖的带有跳跃连接的双卷积盲去噪网络（DCBDNet），能够在去噪效果与网络复杂度之间实现理想平衡。所提DCBDNet由噪声估计网络和双卷积神经网络（CNN）组成。噪声估计网络用于估计噪声水平图，增强了模型的灵活性。双CNN包含两个分支：上分支设计为U型子网络，下分支由扩张卷积层构成。上下分支均使用层间跳跃连接。我们在多个合成和真实图像去噪基准数据集上对DCBDNet进行了评估。实验结果表明，所提DCBDNet能够有效去除宽范围水平的加性高斯噪声、空间变化噪声及真实噪声。尽管模型结构简单，但DCBDNet仍能获得与包含复杂架构的最先进图像去噪模型相竞争的去噪性能。换言之，该方法实现了去噪性能与模型复杂度之间的有利权衡。代码可在 https://github.com/WenCongWu/DCBDNet 获取。