CHO, SUNGHYUN

KIM, GEONUNG

KIM, JUNGEON

LEE, JUNYONG

LEE, SEUNGYONG

RIM, JAESUNG

arXiv

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.Comment: ECCV 2022,Project page: http://cg.postech.ac.kr/research/rsblur

Rim, Jaesung

Kim, Geonung

Kim, Jungeon

Lee, Junyong

Lee, Seungyong

Cho, Sunghyun

arXiv.org e-Print Archive

Realistic Blur Synthesis for Learning Image Deblurring

© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Training learning-based deblurring methods demands a tre-mendous 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.1

포항공과대학교

Realistic Blur Synthesis for Learning Image Deblurring

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