  Motion blur in videos captured by autonomous vehicles and robots can degrade
their perception capability. In this work, we present a novel approach to video
deblurring by fitting a deep network to the test video. Our key observation is
that some frames in a video with motion blur are much sharper than others, and
thus we can transfer the texture information in those sharp frames to blurry
frames. Our approach heuristically selects sharp frames from a video and then
trains a convolutional neural network on these sharp frames. The trained
network often absorbs enough details in the scene to perform deblurring on all
the video frames. As an internal learning method, our approach has no domain
gap between training and test data, which is a problematic issue for existing
video deblurring approaches. The conducted experiments on real-world video data
show that our model can reconstruct clearer and sharper videos than
state-of-the-art video deblurring approaches. Code and data are available at
https://github.com/xrenaa/Deblur-by-Fitting.
