论文标题
封闭视频降级的复发单元
Gated Recurrent Unit for Video Denoising
论文作者
论文摘要
当前的视频降解方法通过设计卷积神经网络（CNN）或将空间降解与时间融合到基本的复发神经网络（RNN）来执行时间融合。但是，尚未有一些适应视频降解的封闭式复发单元（GRU）机制的作品。在这封信中，我们提出了一个基于Gru的新视频Denoising模型，即Gru-VD。首先，使用重置门来标记上一个帧输出中与当前帧相关的内容。然后，隐藏的激活是在标记相关内容的帮助下作为初始的时空denoing。最后，更新门会递归地将初始剥离结果与先前的帧输出融合，以进一步提高精度。为了自适应处理各种光条件，当前框架的噪声标准偏差也被馈送到这三个模块。采用加权损失来同时调节初始脱泽和最终融合。实验结果表明，GRU-VD网络不仅可以客观地和主观上的艺术状态获得更好的质量，而且可以在真实视频上获得满意的主观质量。
Current video denoising methods perform temporal fusion by designing convolutional neural networks (CNN) or combine spatial denoising with temporal fusion into basic recurrent neural networks (RNNs). However, there have not yet been works which adapt gated recurrent unit (GRU) mechanisms for video denoising. In this letter, we propose a new video denoising model based on GRU, namely GRU-VD. First, the reset gate is employed to mark the content related to the current frame in the previous frame output. Then the hidden activation works as an initial spatial-temporal denoising with the help from the marked relevant content. Finally, the update gate recursively fuses the initial denoised result with previous frame output to further increase accuracy. To handle various light conditions adaptively, the noise standard deviation of the current frame is also fed to these three modules. A weighted loss is adopted to regulate initial denoising and final fusion at the same time. The experimental results show that the GRU-VD network not only can achieve better quality than state of the arts objectively and subjectively, but also can obtain satisfied subjective quality on real video.
