Non-local self-similarity is well-known to be an effective prior for the image denoising problem. However, little work has been done to incorporate it in convolutional neural networks, which surpass non-local model-based methods despite only exploiting local information. In this paper, we propose a novel end-to-end trainable neural network architecture employing layers based on graph convolution operations, thereby creating neurons with non-local receptive fields. The graph convolution operation generalizes the classic convolution to arbitrary graphs. In this work, the graph is dynamically computed from similarities among the hidden features of the network, so that the powerful representation learning capabilities of the network are exploited to uncover self-similar patterns. We introduce a lightweight Edge-Conditioned Convolution which addresses vanishing gradient and over-parameterization issues of this particular graph convolution. Extensive experiments show state-of-the-art performance with improved qualitative and quantitative results on both synthetic Gaussian noise and real noise.

中文翻译：

---

深度图-卷积图像去噪。


众所周知，非局部自相似性是图像去噪问题的有效先验。然而，将其合并到卷积神经网络中的工作却很少，尽管仅利用局部信息，但卷积神经网络还是超越了基于非局部模型的方法。在本文中，我们提出了一种新颖的端到端可训练神经网络架构，采用基于图卷积运算的层，从而创建具有非局部感受野的神经元。图卷积运算将经典卷积推广到任意图。在这项工作中，图是根据网络隐藏特征之间的相似性动态计算的，以便利用网络强大的表示学习能力来发现自相似模式。我们引入了一种轻量级的边缘条件卷积，它解决了这种特定图卷积的梯度消失和过度参数化问题。大量的实验表明，合成高斯噪声和真实噪声的定性和定量结果均得到改善，具有最先进的性能。