Abstract
In this paper, we propose a residual non-local attention network for high-quality image restoration. Without considering the uneven distribution of information in the corrupted images, previous methods are restricted by local convolutional operation and equal treatment of spatial- and channel-wise features. To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts. Specifically, we design trunk branch and (non-)local mask branch in each (non-)local attention block. The trunk branch is used to extract hierarchical features. Local and non-local mask branches aim to adaptively rescale these hierarchical features with mixed attentions. The local mask branch concentrates on more local structures with convolutional operations, while non-local attention considers more about long-range dependencies in the whole feature map. Furthermore, we propose residual local and non-local attention learning to train the very deep network, which further enhance the representation ability of the network. Our proposed method can be generalized for various image restoration applications, such as image denoising, demosaicing, compression artifacts reduction, and super-resolution. Experiments demonstrate that our method obtains comparable or better results compared with recently leading methods quantitatively and visually.
Abstract (translated)
本文提出了一种用于高质量图像恢复的剩余非局部关注网络。在不考虑图像中信息分布不均匀的情况下，以前的方法受到局部卷积运算和空间和信道特征的平等处理的限制。为了解决这个问题，我们设计了本地和非本地注意块，以提取捕获像素之间长期依赖关系的功能，并更加关注具有挑战性的部分。具体来说，我们在每个（非）本地注意块中设计了主干分支和（非）本地掩码分支。主干分支用于提取分层特征。局部和非局部遮罩分支的目标是自适应地重新调整这些层次特征，并引起混合关注。局部掩模分支通过卷积运算集中于更多的局部结构，而非局部注意则在整个特征图中考虑更多的长期依赖性。此外，我们还提出了剩余的局部和非局部注意学习来训练非常深的网络，这进一步提高了网络的表示能力。我们提出的方法可以推广到各种图像恢复应用，如图像去噪、去噪、压缩伪影减少和超分辨率。实验表明，该方法在定量和直观上均优于目前的主流方法。
URL
https://arxiv.org/abs/1903.10082
