Generative adversarial network (GAN) for super-resolution (SR) has attracted enormous interest in recent years. It has been widely used to solve the single-image super-resolution (SISR) task and made superior performance. However, GAN is rarely used for video super-resolution (VSR). VSR aims to improve video resolution by exploiting the temporal continuity and spatial similarity of video sequence frames. We design a GAN with multi-feature discriminators and combine it with optical flow estimation compensation to construct an end-to-end VSR framework OFC-MFGAN. Optical flow estimation compensation makes use of temporal continuity and spatial similarity features of adjacent frames to provide rich detailed information for GAN. Multi-feature discriminators based on visual attention mechanism include the pixel discriminator, edge discriminator, gray discriminator, and color discriminator. GAN with multi-feature discriminators makes the data distribution and visually sensitive features (edge, texture, and color) of SR frames similar to high-resolution frames. OFC-MFGAN effectively integrates the time, space, and visually sensitive features of videos. Extensive experiments on public video datasets and surveillance videos show the effectiveness and robustness of the proposed method. Compared with several state-of-the-art VSR methods and SISR methods, the proposed method can not only recover prominent edges, clear textures, and realistic colors but also make a pleasant visual feeling and competitive perceptual index.

中文翻译：

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时空特征补偿与多特征鉴别器相结合，以提高视频超分辨率感知质量

近年来，用于超分辨率（SR）的生成对抗网络（GAN）引起了极大的兴趣。它已被广泛用于解决单图像超分辨率（SISR）任务并取得了卓越的性能。然而，GAN 很少用于视频超分辨率（VSR）。VSR 旨在通过利用视频序列帧的时间连续性和空间相似性来提高视频分辨率。我们设计了一个具有多特征鉴别器的 GAN，并将其与光流估计补偿相结合，构建了一个端到端的 VSR 框架 OFC-MFGAN。光流估计补偿利用相邻帧的时间连续性和空间相似性特征为 GAN 提供丰富的详细信息。基于视觉注意机制的多特征判别器包括像素判别器、边缘判别器、灰色鉴别器和颜色鉴别器。具有多特征鉴别器的 GAN 使 SR 帧的数据分布和视觉敏感特征（边缘、纹理和颜色）类似于高分辨率帧。OFC-MFGAN 有效地整合了视频的时间、空间和视觉敏感特征。对公共视频数据集和监控视频的大量实验表明了所提出方法的有效性和鲁棒性。与几种最先进的 VSR 方法和 SISR 方法相比，所提出的方法不仅可以恢复突出的边缘、清晰的纹理和逼真的色彩，而且还可以获得令人愉悦的视觉感受和有竞争力的感知指数。和颜色）的 SR 帧类似于高分辨率帧。OFC-MFGAN 有效地整合了视频的时间、空间和视觉敏感特征。对公共视频数据集和监控视频的大量实验表明了所提出方法的有效性和鲁棒性。与几种最先进的 VSR 方法和 SISR 方法相比，所提出的方法不仅可以恢复突出的边缘、清晰的纹理和逼真的色彩，而且还可以获得令人愉悦的视觉感受和有竞争力的感知指数。和颜色）的 SR 帧类似于高分辨率帧。OFC-MFGAN 有效地整合了视频的时间、空间和视觉敏感特征。对公共视频数据集和监控视频的大量实验表明了所提出方法的有效性和鲁棒性。与几种最先进的 VSR 方法和 SISR 方法相比，所提出的方法不仅可以恢复突出的边缘、清晰的纹理和逼真的色彩，而且还可以获得令人愉悦的视觉感受和有竞争力的感知指数。