Abstract
Non-uniform blind deblurring for general dynamic
scenes is a challenging computer vision problem as blurs
arise not only from multiple object motions but also from
camera shake, scene depth variation. To remove these
complicated motion blurs, conventional energy optimization based methods rely on simple assumptions such that
blur kernel is partially uniform or locally linear. Moreover, recent machine learning based methods also depend
on synthetic blur datasets generated under these assumptions. This makes conventional deblurring methods fail to
remove blurs where blur kernel is difficult to approximate or
parameterize (e.g. object motion boundaries). In this work,
we propose a multi-scale convolutional neural network that
restores sharp images in an end-to-end manner where blur
is caused by various sources. Together, we present multiscale loss function that mimics conventional coarse-to-fine
approaches. Furthermore, we propose a new large-scale
dataset that provides pairs of realistic blurry image and the
corresponding ground truth sharp image that are obtained
by a high-speed camera. With the proposed model trained
on this dataset, we demonstrate empirically that our method
achieves the state-of-the-art performance in dynamic scene
deblurring not only qualitatively, but also quantitatively