  In this paper, we examine the problem of real-world image deblurring and take
into account two key factors for improving the performance of the deep image
deblurring model, namely, training data synthesis and network architecture
design. Deblurring models trained on existing synthetic datasets perform poorly
on real blurry images due to domain shift. To reduce the domain gap between
synthetic and real domains, we propose a novel realistic blur synthesis
pipeline to simulate the camera imaging process. As a result of our proposed
synthesis method, existing deblurring models could be made more robust to
handle real-world blur. Furthermore, we develop an effective deblurring model
that captures non-local dependencies and local context in the feature domain
simultaneously. Specifically, we introduce the multi-path transformer module to
UNet architecture for enriched multi-scale features learning. A comprehensive
experiment on three real-world datasets shows that the proposed deblurring
model performs better than state-of-the-art methods.
