论文标题
实际视频超分辨率模型中的缓解工件
Mitigating Artifacts in Real-World Video Super-Resolution Models
论文作者
论文摘要
复发结构是视频超分辨率任务的普遍框架，该框架通过隐藏状态对帧之间的时间依赖性进行建模。当应用于具有未知和复杂降解的现实世界情景时，隐藏状态倾向于包含不愉快的人工制品并将其传播到恢复的框架。在这种情况下，我们的分析表明，当隐藏状态被更清洁的同类用品代替时，可以在很大程度上减轻此类文物。根据观察结果，我们提出了隐藏的状态注意（HSA）模块，以减轻现实世界视频超分辨率中的人工制品。具体来说，我们首先采用各种廉价过滤器来生产隐藏的州池。例如，高斯模糊过滤器用于平滑伪像，而锐化过滤器则用于增强细节。为了汇总一个新的隐藏状态，其中包含隐藏状态池中较少的工件，我们设计了一个选择性交叉注意（SCA）模块，其中计算输入功能和每个隐藏状态之间的注意力。配备了HSA，我们提出的方法，即Fastrealvsr，能够达到2倍的速度，同时获得比Real-BasicVSR更好的性能。代码将在https://github.com/tencentarc/fastrealvsr上找到
The recurrent structure is a prevalent framework for the task of video super-resolution, which models the temporal dependency between frames via hidden states. When applied to real-world scenarios with unknown and complex degradations, hidden states tend to contain unpleasant artifacts and propagate them to restored frames. In this circumstance, our analyses show that such artifacts can be largely alleviated when the hidden state is replaced with a cleaner counterpart. Based on the observations, we propose a Hidden State Attention (HSA) module to mitigate artifacts in real-world video super-resolution. Specifically, we first adopt various cheap filters to produce a hidden state pool. For example, Gaussian blur filters are for smoothing artifacts while sharpening filters are for enhancing details. To aggregate a new hidden state that contains fewer artifacts from the hidden state pool, we devise a Selective Cross Attention (SCA) module, in which the attention between input features and each hidden state is calculated. Equipped with HSA, our proposed method, namely FastRealVSR, is able to achieve 2x speedup while obtaining better performance than Real-BasicVSR. Codes will be available at https://github.com/TencentARC/FastRealVSR
