While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising, existing methods mostly rely on simple noise assumptions, such as additive white Gaussian noise (AWGN), JPEG compression noise and camera sensor noise, and a general-purpose blind denoising method for real images remains unsolved. In this paper, we attempt to solve this problem from the perspective of network architecture design and training data synthesis. Specifically, for the network architecture design, we propose a swin-conv block to incorporate the local modeling ability of residual convolutional layer and non-local modeling ability of swin transformer block, and then plug it as the main building block into the widely-used image-to-image translation UNet architecture. For the training data synthesis, we design a practical noise degradation model which takes into consideration different kinds of noise (including Gaussian, Poisson, speckle, JPEG compression, and processed camera sensor noises) and resizing, and also involves a random shuffle strategy and a double degradation strategy. Extensive experiments on AGWN removal and real image denoising demonstrate that the new network architecture design achieves state-of-the-art performance and the new degradation model can help to significantly improve the practicability. We believe our work can provide useful insights into current denoising research.

翻译：尽管近年来利用深度神经网络解决图像去噪问题取得了显著进展，但现有方法大多依赖于简单噪声假设（如加性高斯白噪声、JPEG压缩噪声和相机传感器噪声），针对真实图像的全方位盲去噪方法仍未解决。本文试图从网络架构设计和训练数据合成两个角度解决该问题。具体而言，在网络架构设计方面，我们提出了一种Swin-Conv模块，该模块融合了残差卷积层的局部建模能力与Swin Transformer块的非局部建模能力，并将其作为主要构建块嵌入广泛使用的图像到图像转换UNet架构中。在训练数据合成方面，我们设计了一种实用的噪声退化模型，该模型综合考虑了多种噪声类型（包括高斯噪声、泊松噪声、散斑噪声、JPEG压缩噪声及处理后的相机传感器噪声）以及缩放操作，并引入了随机洗牌策略和双重退化策略。在AWGN去除和真实图像去噪上的大量实验表明，新网络架构设计达到了最先进的性能，而新退化模型能够显著提升实用性。我们相信，这项工作可为当前的去噪研究提供有价值的见解。