  The goal of dynamic scene deblurring is to remove the motion blur present in
a given image. Most learning-based approaches implement their solutions by
minimizing the L1 or L2 distance between the output and reference sharp image.
Recent attempts improve the perceptual quality of the deblurred image by using
features learned from visual recognition tasks. However, those features are
originally designed to capture the high-level contexts rather than the
low-level structures of the given image, such as blurriness. We propose a novel
low-level perceptual loss to make image sharper. To better focus on image
blurriness, we train a reblurring module amplifying the unremoved motion blur.
Motivated that a well-deblurred clean image should contain zero-magnitude
motion blur that is hard to be amplified, we design two types of reblurring
loss functions. The supervised reblurring loss at training stage compares the
amplified blur between the deblurred image and the reference sharp image. The
self-supervised reblurring loss at inference stage inspects if the deblurred
image still contains noticeable blur to be amplified. Our experimental results
demonstrate the proposed reblurring losses improve the perceptual quality of
the deblurred images in terms of NIQE and LPIPS scores as well as visual
sharpness.
