  Applying image processing algorithms independently to each video frame often
leads to temporal inconsistency in the resulting video. To address this issue,
we present a novel and general approach for blind video temporal consistency.
Our method is only trained on a pair of original and processed videos directly
instead of a large dataset. Unlike most previous methods that enforce temporal
consistency with optical flow, we show that temporal consistency can be
achieved by training a convolutional network on a video with the Deep Video
Prior. Moreover, a carefully designed iteratively reweighted training strategy
is proposed to address the challenging multimodal inconsistency problem. We
demonstrate the effectiveness of our approach on 7 computer vision tasks on
videos. Extensive quantitative and perceptual experiments show that our
approach obtains superior performance than state-of-the-art methods on blind
video temporal consistency. Our source codes are publicly available at
github.com/ChenyangLEI/deep-video-prior.
