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Joint rolling shutter correction and deblurring (RSCD) techniques arecritical for the prevalent CMOS cameras. However, current approaches are stillbased on conventional energy optimization and are developed for static scenes.To enable learning-based approaches to address real-world RSCD problem, wecontribute the first dataset, BS-RSCD, which includes both ego-motion andobject-motion in dynamic scenes. Real distorted and blurry videos withcorresponding ground truth are recorded simultaneously via abeam-splitter-based acquisition system.  Since direct application of existing individual rolling shutter correction(RSC) or global shutter deblurring (GSD) methods on RSCD leads to undesirableresults due to inherent flaws in the network architecture, we further presentthe first learning-based model (JCD) for RSCD. The key idea is that we adoptbi-directional warping streams for displacement compensation, while alsopreserving the non-warped deblurring stream for details restoration. Theexperimental results demonstrate that JCD achieves state-of-the-art performanceon the realistic RSCD dataset (BS-RSCD) and the synthetic RSC dataset(Fastec-RS). The dataset and code are available athttps://github.com/zzh-tech/RSCD.

Towards Rolling Shutter Correction and Deblurring in Dynamic Scenes