论文标题
在视频超分辨率中，空间与时间之间是否有权衡？
Is There Tradeoff between Spatial and Temporal in Video Super-Resolution?
论文作者
论文摘要
深度学习的最新进展导致了基于卷积神经网络（CNN）的图像和视频超分辨率（SR）方法的巨大成功。对于视频SR，已经提出了高级算法来利用低分辨率（LR）视频帧之间的时间相关性和/或具有多个LR框架的帧。这些方法追求更高质量的超级分辨框架，其中通常按框架测量质量。 psnr。但是，框架质量可能无法揭示帧之间的一致性。如果独立地应用算法（大多数以前方法的情况），则该算法可能会导致时间不一致，可以将其视为闪烁。这是提高框架忠诚度和框架间一致性的自然要求，分别称为空间质量和时间质量。然后我们可能会问，是否针对空间质量进行了优化的方法也针对时间质量进行了优化？我们可以共同优化两个质量指标吗？
Recent advances of deep learning lead to great success of image and video super-resolution (SR) methods that are based on convolutional neural networks (CNN). For video SR, advanced algorithms have been proposed to exploit the temporal correlation between low-resolution (LR) video frames, and/or to super-resolve a frame with multiple LR frames. These methods pursue higher quality of super-resolved frames, where the quality is usually measured frame by frame in e.g. PSNR. However, frame-wise quality may not reveal the consistency between frames. If an algorithm is applied to each frame independently (which is the case of most previous methods), the algorithm may cause temporal inconsistency, which can be observed as flickering. It is a natural requirement to improve both frame-wise fidelity and between-frame consistency, which are termed spatial quality and temporal quality, respectively. Then we may ask, is a method optimized for spatial quality also optimized for temporal quality? Can we optimize the two quality metrics jointly?
