  Training learning-based deblurring methods demands a tremendous amount of
blurred and sharp image pairs. Unfortunately, existing synthetic datasets are
not realistic enough, and deblurring models trained on them cannot handle real
blurred images effectively. While real datasets have recently been proposed,
they provide limited diversity of scenes and camera settings, and capturing
real datasets for diverse settings is still challenging. To resolve this, this
paper analyzes various factors that introduce differences between real and
synthetic blurred images. To this end, we present RSBlur, a novel dataset with
real blurred images and the corresponding sharp image sequences to enable a
detailed analysis of the difference between real and synthetic blur. With the
dataset, we reveal the effects of different factors in the blur generation
process. Based on the analysis, we also present a novel blur synthesis pipeline
to synthesize more realistic blur. We show that our synthesis pipeline can
improve the deblurring performance on real blurred images.
