论文标题
使用专家网络的混合物恢复空间异质失真
Restoring Spatially-Heterogeneous Distortions using Mixture of Experts Network
论文作者
论文摘要
近年来，基于深度学习的方法已成功应用于图像失真恢复任务。但是，假定单个失真的方案可能不适合许多现实世界应用。为了处理这种情况，一些研究提出了依次合并的扭曲数据集。在组合的不同点上查看，我们引入了一个空间异构的失真数据集，其中将多个损坏应用于每个图像的不同位置。此外，我们还提出了专家网络的混合物，以有效恢复多启动图像。由多任务学习的激励，我们设计了我们的网络，以拥有多个学习常见和特定于失真的表示的途径。我们的模型可有效地恢复现实世界的扭曲，我们在实验上验证了我们的方法是否优于旨在管理单个失真和多种变形的其他模型。
In recent years, deep learning-based methods have been successfully applied to the image distortion restoration tasks. However, scenarios that assume a single distortion only may not be suitable for many real-world applications. To deal with such cases, some studies have proposed sequentially combined distortions datasets. Viewing in a different point of combining, we introduce a spatially-heterogeneous distortion dataset in which multiple corruptions are applied to the different locations of each image. In addition, we also propose a mixture of experts network to effectively restore a multi-distortion image. Motivated by the multi-task learning, we design our network to have multiple paths that learn both common and distortion-specific representations. Our model is effective for restoring real-world distortions and we experimentally verify that our method outperforms other models designed to manage both single distortion and multiple distortions.
