  Contaminants such as dust, dirt and moisture adhering to the camera lens can
greatly affect the quality and clarity of the resulting image or video. In this
paper, we propose a video restoration method to automatically remove these
contaminants and produce a clean video. Our approach first seeks to detect
attention maps that indicate the regions that need to be restored. In order to
leverage the corresponding clean pixels from adjacent frames, we propose a flow
completion module to hallucinate the flow of the background scene to the
attention regions degraded by the contaminants. Guided by the attention maps
and completed flows, we propose a recurrent technique to restore the input
frame by fetching clean pixels from adjacent frames. Finally, a multi-frame
processing stage is used to further process the entire video sequence in order
to enforce temporal consistency. The entire network is trained on a synthetic
dataset that approximates the physical lighting properties of contaminant
artifacts. This new dataset and our novel framework lead to our method that is
able to address different contaminants and outperforms competitive restoration
approaches both qualitatively and quantitatively.
