  Joint rolling shutter correction and deblurring (RSCD) techniques are
critical for the prevalent CMOS cameras. However, current approaches are still
based on conventional energy optimization and are developed for static scenes.
To enable learning-based approaches to address real-world RSCD problem, we
contribute the first dataset, BS-RSCD, which includes both ego-motion and
object-motion in dynamic scenes. Real distorted and blurry videos with
corresponding ground truth are recorded simultaneously via a
beam-splitter-based acquisition system.
  Since direct application of existing individual rolling shutter correction
(RSC) or global shutter deblurring (GSD) methods on RSCD leads to undesirable
results due to inherent flaws in the network architecture, we further present
the first learning-based model (JCD) for RSCD. The key idea is that we adopt
bi-directional warping streams for displacement compensation, while also
preserving the non-warped deblurring stream for details restoration. The
experimental results demonstrate that JCD achieves state-of-the-art performance
on the realistic RSCD dataset (BS-RSCD) and the synthetic RSC dataset
(Fastec-RS). The dataset and code are available at
https://github.com/zzh-tech/RSCD.
