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
this https URL

我们提供了第一个数据集 BS-RSCD，包含具有动态场景中的自我运动和对象运动的实际扭曲和模糊视频，我们进一步提出了第一个学习模型 JCD，通过采用双向翘曲流进行位移补偿，并同时保留非翘曲去模糊流以进行细节恢复，我们的实验结果表明 JCD 在现实 RSCD 数据集（BS-RSCD）和 RSC 合成数据集（Fastec-RS）上实现了最先进的性能。