  One of the key components for video deblurring is how to exploit neighboring
frames. Recent state-of-the-art methods either used aligned adjacent frames to
the center frame or propagated the information on past frames to the current
frame recurrently. Here we propose multi-blur-to-deblur (MB2D), a novel concept
to exploit neighboring frames for efficient video deblurring. Firstly, inspired
by unsharp masking, we argue that using more blurred images with long exposures
as additional inputs significantly improves performance. Secondly, we propose
multi-blurring recurrent neural network (MBRNN) that can synthesize more
blurred images from neighboring frames, yielding substantially improved
performance with existing video deblurring methods. Lastly, we propose
multi-scale deblurring with connecting recurrent feature map from MBRNN (MSDR)
to achieve state-of-the-art performance on the popular GoPro and Su datasets in
fast and memory efficient ways.
