  Motion deblurring has witnessed rapid development in recent years, and most
of the recent methods address it by using deep learning techniques, with the
help of different kinds of prior knowledge. Concerning that deblurring is
essentially expected to improve the image sharpness, edge information can serve
as an important prior. However, the edge has not yet been seriously taken into
consideration in previous methods when designing deep models. To this end, we
present a novel framework that incorporates edge prior knowledge into deep
models, termed Edge Prior Augmented Networks (EPAN). EPAN has a content-based
main branch and an edge-based auxiliary branch, which are constructed as a
Content Deblurring Net (CDN) and an Edge Enhancement Net (EEN), respectively.
EEN is designed to augment CDN in the deblurring process via an attentive
fusion mechanism, where edge features are mapped as spatial masks to guide
content features in a feature-based hierarchical manner. An edge-guided loss
function is proposed to further regulate the optimization of EPAN by enforcing
the focus on edge areas. Besides, we design a dual-camera-based image capturing
setting to build a new dataset, Real Object Motion Blur (ROMB), with paired
sharp and naturally blurry images of fast-moving cars, so as to better train
motion deblurring models and benchmark the capability of motion deblurring
algorithms in practice. Extensive experiments on the proposed ROMB and other
existing datasets demonstrate that EPAN outperforms state-of-the-art approaches
qualitatively and quantitatively.
