  The goal of dynamic scene deblurring is to remove the motion blur in a given
image. Typical learning-based approaches implement their solutions by
minimizing the L1 or L2 distance between the output and the reference sharp
image. Recent attempts adopt visual recognition features in training to improve
the perceptual quality. However, those features are primarily designed to
capture high-level contexts rather than low-level structures such as
blurriness. Instead, we propose a more direct way to make images sharper by
exploiting the inverse task of deblurring, namely, reblurring. Reblurring
amplifies the remaining blur to rebuild the original blur, however, a
well-deblurred clean image with zero-magnitude blur is hard to reblur. Thus, we
design two types of reblurring loss functions for better deblurring. The
supervised reblurring loss at training stage compares the amplified blur
between the deblurred and the sharp images. The self-supervised reblurring loss
at inference stage inspects if there noticeable blur remains in the deblurred.
Our experimental results on large-scale benchmarks and real images demonstrate
the effectiveness of the reblurring losses in improving the perceptual quality
of the deblurred images in terms of NIQE and LPIPS scores as well as visual
sharpness.
