论文标题
用于超分辨率的多模式数据集
Multi-modal Datasets for Super-resolution
论文作者
论文摘要
如今，大多数用于训练和评估超分辨率模型的数据集是单模式模拟数据集。但是，由于现实世界中各种图像降解类型的多样性，在单模式模拟数据集中训练的模型在不同的降解场景中并不总是具有良好的鲁棒性和概括能力。先前的工作倾向于只关注真实的图像。相比之下，我们首先提出了用于超分辨率（OID-RW）的现实世界黑白旧照片数据集，该数据集使用两种手动填充像素和用不同相机拍摄的方法构造。该数据集包含82组图像，包括22组字符类型和60组景观和架构。同时，我们还提出了一个多模式降解数据集（MDD400），以在现实生活中的图像退化方案中求解超分辨率重建。我们设法通过以下四种方法模拟了生成降级图像的过程：插值算法，CNN网络，GAN网络和以不同的比特率捕获视频。我们的实验表明，不仅在我们数据集上训练的模型具有更好的概括能力和鲁棒性，而且训练有素的图像也可以保持更好的边缘轮廓和纹理功能。
Nowdays, most datasets used to train and evaluate super-resolution models are single-modal simulation datasets. However, due to the variety of image degradation types in the real world, models trained on single-modal simulation datasets do not always have good robustness and generalization ability in different degradation scenarios. Previous work tended to focus only on true-color images. In contrast, we first proposed real-world black-and-white old photo datasets for super-resolution (OID-RW), which is constructed using two methods of manually filling pixels and shooting with different cameras. The dataset contains 82 groups of images, including 22 groups of character type and 60 groups of landscape and architecture. At the same time, we also propose a multi-modal degradation dataset (MDD400) to solve the super-resolution reconstruction in real-life image degradation scenarios. We managed to simulate the process of generating degraded images by the following four methods: interpolation algorithm, CNN network, GAN network and capturing videos with different bit rates. Our experiments demonstrate that not only the models trained on our dataset have better generalization capability and robustness, but also the trained images can maintain better edge contours and texture features.
