Real‐world image deblurring using data synthesis and feature complementary network
Hao Wei,
Chenyang Ge,
Xin Qiao
et al.
Abstract:Many learning‐based approaches to image deblurring have received increasing attention in recent years. However, the models trained on existing synthetic datasets do not generalize well to real‐world blur, resulting in undesirable artifacts and residual blur. This work attempts to address this problem from two aspects: training data synthesis and network architecture. To narrow the domain gap between synthetic and real domains, a realistic blur synthesis pipeline to generate high‐quality blurred data is propose… Show more
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