Motion blur is a frequently observed image artifact, especially under
insufficient illumination where exposure time has to be prolonged so as to
collect more photons for a bright enough image. Rather than simply removing
such blurring effects, recent researches have aimed at decomposing a blurry
image into multiple sharp images with spatial and temporal coherence. Since
motion blur decomposition itself is highly ambiguous, priors from neighbouring
frames or human annotation are usually needed for motion disambiguation. In
this paper, inspired by the complementary exposure characteristics of a global
shutter (GS) camera and a rolling shutter (RS) camera, we propose to utilize
the ordered scanline-wise delay in a rolling shutter image to robustify motion
decomposition of a single blurry image. To evaluate this novel dual imaging
setting, we construct a triaxial system to collect realistic data, as well as a
deep network architecture that explicitly addresses temporal and contextual
information through reciprocal branches for cross-shutter motion blur
decomposition. Experiment results have verified the effectiveness of our
proposed algorithm, as well as the validity of our dual imaging setting.

基于滚动快门图像的有序扫描线延迟，我们提出利用滚动快门和全局快门相机的曝光特性，对单个模糊图像进行稳健的运动分解。通过构建一个拥有现实数据的三轴系统和一个深度网络架构，通过交叉快门运动模糊分解的循环分支来明确处理时间和上下文信息。实验证实了我们提出的算法的有效性，以及我们的双摄影设置的可行性。

带有交互快门指导的运动模糊分解

Motion Blur Decomposition with Cross-shutter Guidance

With the ubiquity of rolling shutter (RS) cameras, it is becoming
increasingly attractive to recover the latent global shutter (GS) video from
two consecutive RS frames, which also places a higher demand on realism.
Existing solutions, using deep neural networks or optimization, achieve
promising performance. However, these methods generate intermediate GS frames
through image warping based on the RS model, which inevitably result in black
holes and noticeable motion artifacts. In this paper, we alleviate these issues
by proposing a context-aware GS video reconstruction architecture. It
facilitates the advantages such as occlusion reasoning, motion compensation,
and temporal abstraction. Specifically, we first estimate the bilateral motion
field so that the pixels of the two RS frames are warped to a common GS frame
accordingly. Then, a refinement scheme is proposed to guide the GS frame
synthesis along with bilateral occlusion masks to produce high-fidelity GS
video frames at arbitrary times. Furthermore, we derive an approximated
bilateral motion field model, which can serve as an alternative to provide a
simple but effective GS frame initialization for related tasks. Experiments on
synthetic and real data show that our approach achieves superior performance
over state-of-the-art methods in terms of objective metrics and subjective
visual quality. Code is available at https://github.com/GitCVfb/CVR.

本文提出了一种上下文感知的全局快门视频重建体系结构，通过估计双边运动场，引导全局快门帧合成，产生任意时间高保真度的全局快门视频帧，并在合成任务的相关任务中提供简单而有效的全局快门帧初始化。

滚动快门相机的上下文感知视频重建

Context-Aware Video Reconstruction for Rolling Shutter Cameras

Modern automotive vehicles are often equipped with a budget commercial
rolling shutter camera. These devices often produce distorted images due to the
inter-row delay of the camera while capturing the image. Recent methods for
monocular rolling shutter motion compensation utilize blur kernel and the
straightness property of line segments. However, these methods are limited to
handling rotational motion and also are not fast enough to operate in real
time. In this paper, we propose a minimal solver for the rolling shutter motion
compensation which assumes known vertical direction of the camera. Thanks to
the Ackermann motion model of vehicles which consists of only two motion
parameters, and two parameters for the simplified depth assumption that lead to
a 4-line algorithm. The proposed minimal solver estimates the rolling shutter
camera motion efficiently and accurately. The extensive experiments on real and
simulated datasets demonstrate the benefits of our approach in terms of
qualitative and quantitative results.

本研究提出了一种基于 Ackermann 运动模型和简化深度假设的最小算法求解滚动快门相机拍摄影像中的畸变，实现了高效且精确的运动补偿。