论文标题
深度参数3D过滤器，用于视频的联合视频降级和照明增强视频超级分辨率
Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super Resolution
论文作者
论文摘要
尽管最近的方法带来了质量的提高，但视频超分辨率（SR）仍然非常具有挑战性，尤其是对于低光和嘈杂的视频。当前的最佳解决方案是随后采用最佳的视频SR模型，Denoising和Illumination Enerancions，但由于模型之间的不一致，因此通常会降低图像质量。本文提出了一种称为“深参数3D过滤器（DP3DF）”的新参数表示，该表示包含局部时空信息，以在单个编码器和编码器网络中有效地启用同时降解，照明增强和SR。此外，通过共享主链共同学习了一个动态残留框架，以进一步提高SR质量。我们进行了广泛的实验，包括大规模的用户研究，以表明我们的方法的有效性。我们的方法一致地超过了所有具有顶级PSNR和用户评分的挑战性的真实数据集上最好的最新方法，但运行时间很快。
Despite the quality improvement brought by the recent methods, video super-resolution (SR) is still very challenging, especially for videos that are low-light and noisy. The current best solution is to subsequently employ best models of video SR, denoising, and illumination enhancement, but doing so often lowers the image quality, due to the inconsistency between the models. This paper presents a new parametric representation called the Deep Parametric 3D Filters (DP3DF), which incorporates local spatiotemporal information to enable simultaneous denoising, illumination enhancement, and SR efficiently in a single encoder-and-decoder network. Also, a dynamic residual frame is jointly learned with the DP3DF via a shared backbone to further boost the SR quality. We performed extensive experiments, including a large-scale user study, to show our method's effectiveness. Our method consistently surpasses the best state-of-the-art methods on all the challenging real datasets with top PSNR and user ratings, yet having a very fast run time.
