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。

朝着无光晕的图像：一项调查

Toward Flare-Free Images: A Survey

When a camera is pointed at a strong light source, the resulting photograph
may contain lens flare artifacts. Flares appear in a wide variety of patterns
(halos, streaks, color bleeding, haze, etc.) and this diversity in appearance
makes flare removal challenging. Existing analytical solutions make strong
assumptions about the artifact's geometry or brightness, and therefore only
work well on a small subset of flares. Machine learning techniques have shown
success in removing other types of artifacts, like reflections, but have not
been widely applied to flare removal due to the lack of training data. To solve
this problem, we explicitly model the optical causes of flare either
empirically or using wave optics, and generate semi-synthetic pairs of
flare-corrupted and clean images. This enables us to train neural networks to
remove lens flare for the first time. Experiments show our data synthesis
approach is critical for accurate flare removal, and that models trained with
our technique generalize well to real lens flares across different scenes,
lighting conditions, and cameras.

通过机器学习和数据合成技术，我们能够第一次成功地训练神经网络来去除镜头光晕，从而对各种不同模式的光斑进行了更加准确的去除。