Edge Prior Augmented Networks for Motion Deblurring on Naturally Blurry Images

TL;DR

This paper introduces EPAN, a novel deep learning framework that incorporates edge prior knowledge for improved motion deblurring of naturally blurry images, utilizing an auxiliary edge enhancement network and a new dataset.

Contribution

The paper proposes a new edge prior augmented network with an auxiliary edge enhancement branch and an edge-guided loss, along with a new dataset for motion deblurring of real-world images.

Findings

EPAN outperforms existing methods on multiple datasets.

The edge-guided loss improves focus on edge regions.

The new ROMB dataset enables better training and benchmarking.

Abstract

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.