Reference-based Motion Blur Removal: Learning to Utilize Sharpness in
  the Reference Image
Han Zou Masanori Suganuma Takayuki Okatani
Despite the recent advancement in the study of removing motion blur in an
image, it is still hard to deal with strong blurs. While there are limits in
removing blurs from a single image, it has more potential to use multiple
images, e.g., using an additional image as a reference to deblur a blurry
image. A typical setting is deburring an image using a nearby sharp image(s) in
a video sequence, as in the studies of video deblurring. This paper proposes a
better method to use the information present in a reference image. The method
does not need a strong assumption on the reference image. We can utilize an
alternative shot of the identical scene, just like in video deblurring, or we
can even employ a distinct image from another scene. Our method first matches
local patches of the target and reference images and then fuses their features
to estimate a sharp image. We employ a patch-based feature matching strategy to
solve the difficult problem of matching the blurry image with the sharp
reference. Our method can be integrated into pre-existing networks designed for
single image deblurring. The experimental results show the effectiveness of the
proposed method.