Abstract
Blind motion deblurring methods are primarily responsible for recovering an accurate estimate of the blur kernel.
Non-blind deblurring (NBD) methods, on the other hand,
attempt to faithfully restore the original image, given the
blur estimate. However, NBD is quite susceptible to errors
in blur kernel. In this work, we present a convolutional neural network-based approach to handle kernel uncertainty
in non-blind motion deblurring. We provide multiple latent
image estimates corresponding to different prior strengths
obtained from a given blurry observation in order to exploit
the complementarity of these inputs for improved learning.
To generalize the performance to tackle arbitrary kernel
noise, we train our network with a large number of real
and synthetic noisy blur kernels. Our network mitigates
the effects of kernel noise so as to yield detail-preserving
and artifact-free restoration. Our quantitative and qualitative evaluations on benchmark datasets demonstrate that
the proposed method delivers state-of-the-art results. To
further underscore the benefits that can be achieved from
our network, we propose two adaptations of our method to
improve kernel estimates, and image deblurring quality, respectively.