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

Lens flare is a common image artifact that can significantly degrade image
quality and affect the performance of computer vision systems due to a strong
light source pointing at the camera. This survey provides a comprehensive
overview of the multifaceted domain of lens flare, encompassing its underlying
physics, influencing factors, types, and characteristics. It delves into the
complex optics of flare formation, arising from factors like internal
reflection, scattering, diffraction, and dispersion within the camera lens
system. The diverse categories of flare are explored, including scattering,
reflective, glare, orb, and starburst types. Key properties such as shape,
color, and localization are analyzed. The numerous factors impacting flare
appearance are discussed, spanning light source attributes, lens features,
camera settings, and scene content. The survey extensively covers the wide
range of methods proposed for flare removal, including hardware optimization
strategies, classical image processing techniques, and learning-based methods
using deep learning. It not only describes pioneering flare datasets created
for training and evaluation purposes but also how they were created. Commonly
employed performance metrics such as PSNR, SSIM, and LPIPS are explored.
Challenges posed by flare's complex and data-dependent characteristics are
highlighted. The survey provides insights into best practices, limitations, and
promising future directions for flare removal research. Reviewing the
state-of-the-art enables an in-depth understanding of the inherent complexities
of the flare phenomenon and the capabilities of existing solutions. This can
inform and inspire new innovations for handling lens flare artifacts and
improving visual quality across various applications.

镜头光晕是一种常见的图像伪影，可以显著降低图像质量并影响计算机视觉系统的性能。本调查综述了镜头光晕的多方面领域，包括其基本物理、影响因素、类型和特征。研究了光晕形成的复杂光学机制，涉及镜头系统内部反射、散射、衍射和色散等因素。探讨了光晕的各种类型，包括散射、反射、眩光、球体和星芒等。分析了形状、颜色和定位等关键属性。讨论了影响光晕出现的众多因素，包括光源属性、镜头特性、相机设置和场景内容。调查广泛涵盖了用于光晕去除的各种方法，包括硬件优化策略、经典图像处理技术和使用深度学习的基于学习的方法。描述了为训练和评估目的创建的开创性光晕数据集以及它们的创建方式。探讨了常用的性能度量指标，如 PSNR、SSIM 和 LPIPS。突出了光晕复杂且数据相关特征带来的挑战。调查提供了最佳实践、限制和有希望的未来发展方向，以便进行光晕去除研究。通过审视最新的技术发展，可以深入了解光晕现象的固有复杂性和现有解决方案的能力。这可以为处理镜头光晕伪影和提高各种应用的视觉质量提供信息和灵感。