Abstract. Video super-resolution (VSR) is an image restoration task, aiming to reconstruct a high-resolution (HR) video from its down-sampled low-resolution (LR) version. Convolutional neural networks (CNNs) have been applied to VSR successfully. Explicit motion estimation and motion compensation (ME&MC) module is commonly used in the previous CNNs-based methods to better exploit input frames’ temporal similarity. We proposed a VSR network without an explicit ME&MC module. Our network makes full use of spatiotemporal information and can implicitly capture motion relations between frames. Specifically, we proposed an enhanced deep feature extraction module (EDFEM) to extract deep features from input frames. EDFEM exploits not only intra-frame spatial information but also inter-frame temporal information to enhance feature representation. Furthermore, we proposed a residual up-down block (RUDB) to fuse features. RUDB adopts up- and down-sampling layers as the residual branch. Compared to the common residual block, RUDB addresses mutual dependencies of LR and HR images. Visual and quantitative results show that our method achieves state-of-the-art performance.

中文翻译：

---

通过增强的深度特征提取和残差上下块的视频超分辨率网络

摘要。视频超分辨率 (VSR) 是一项图像恢复任务，旨在从其下采样的低分辨率 (LR) 版本重建高分辨率 (HR) 视频。卷积神经网络 (CNN) 已成功应用于 VSR。显式运动估计和运动补偿 (ME&MC) 模块通常用于之前基于 CNN 的方法中，以更好地利用输入帧的时间相似性。我们提出了一个没有显式 ME&MC 模块的 VSR 网络。我们的网络充分利用时空信息，可以隐式捕捉帧之间的运动关系。具体来说，我们提出了一个增强的深度特征提取模块（EDFEM）来从输入帧中提取深度特征。EDFEM 不仅利用帧内空间信息，而且利用帧间时间信息来增强特征表示。此外，我们提出了一个残差上下块（RUDB）来融合特征。RUDB 采用上采样和下采样层作为残差分支。与普通残差块相比，RUDB 解决了 LR 和 HR 图像的相互依赖问题。视觉和定量结果表明，我们的方法达到了最先进的性能。