The popularity of high and ultra-high definition displays has led to the need for methods to improve the quality of videos already obtained at much lower resolutions. A large amount of current CNN-based Video Super-Resolution methods are designed and trained to handle a specific degradation operator (e.g., bicubic downsampling) and are not robust to mismatch between training and testing degradation models. This causes their performance to deteriorate in real-life applications. Furthermore, many of them use the Mean-Squared-Error as the only loss during learning, causing the resulting images to be too smooth. In this work we propose a new Convolutional Neural Network for video super resolution which is robust to multiple degradation models. During training, which is performed on a large dataset of scenes with slow and fast motions, it uses the pseudo-inverse image formation model as part of the network architecture in conjunction with perceptual losses and a smoothness constraint that eliminates the artifacts originating from these perceptual losses. The experimental validation shows that our approach outperforms current state-of-the-art methods and is robust to multiple degradations.

高清晰度和超高清晰度显示器的普及导致需要一种方法来提高已经以低得多的分辨率获得的视频质量。设计和培训了许多当前的基于CNN的视频超分辨率方法，以处理特定的降级运算符（例如，双三次降采样），并且对于训练和测试降级模型之间的不匹配性不强。这导致它们在实际应用中的性能下降。此外，他们中的许多人将均方误差用作学习过程中的唯一损失，从而导致生成的图像过于平滑。在这项工作中，我们提出了一种新的用于视频超分辨率的卷积神经网络，该网络对多种降级模型均具有鲁棒性。在训练过程中，这是对大型场景的慢动作和快动作进行的，它使用伪逆图像形成模型作为网络体系结构的一部分，并结合了感知损失和消除来自这些感知损失的伪像的平滑度约束。实验验证表明，我们的方法优于当前的最新方法，并且对多种降级均具有鲁棒性。