When light is scattered or reflected accidentally in the lens, flare
artifacts may appear in the captured photos, affecting the photos' visual
quality. The main challenge in flare removal is to eliminate various flare
artifacts while preserving the original content of the image. To address this
challenge, we propose a lightweight Multi-Frequency Deflare Network (MFDNet)
based on the Laplacian Pyramid. Our network decomposes the flare-corrupted
image into low and high-frequency bands, effectively separating the
illumination and content information in the image. The low-frequency part
typically contains illumination information, while the high-frequency part
contains detailed content information. So our MFDNet consists of two main
modules: the Low-Frequency Flare Perception Module (LFFPM) to remove flare in
the low-frequency part and the Hierarchical Fusion Reconstruction Module (HFRM)
to reconstruct the flare-free image. Specifically, to perceive flare from a
global perspective while retaining detailed information for image restoration,
LFFPM utilizes Transformer to extract global information while utilizing a
convolutional neural network to capture detailed local features. Then HFRM
gradually fuses the outputs of LFFPM with the high-frequency component of the
image through feature aggregation. Moreover, our MFDNet can reduce the
computational cost by processing in multiple frequency bands instead of
directly removing the flare on the input image. Experimental results
demonstrate that our approach outperforms state-of-the-art methods in removing
nighttime flare on real-world and synthetic images from the Flare7K dataset.
Furthermore, the computational complexity of our model is remarkably low.

通过 Laplacian 金字塔，我们提出了一种轻量级多频去镜头光晕网络 (MFDNet)，将镜头光晕快速而有效地分离为低频和高频部分，并利用 Transformer 提取全局信息与卷积神经网络捕捉局部特征相结合，通过特征聚合逐渐融合两个模块的输出，显著降低计算成本并超越了当前研究中的方法在夜间场景的真实世界和合成图像上去除镜头光晕。

MFDNet：高效夜间闪光去除的多频率网络

MFDNet: Multi-Frequency Deflare Network for Efficient Nighttime Flare  Removal

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

Photographing in the under-illuminated scenes, the presence of complex light
sources often leave strong flare artifacts in images, where the intensity, the
spectrum, the reflection, and the aberration altogether contribute the
deterioration. Besides the image quality, it also influence the performance of
down-stream visual applications. Thus, removing the lens flare and ghosts is a
challenge issue especially in low-light environment. However, existing methods
for flare removal mainly restricted to the problems of inadequate simulation
and real-world capture, where the categories of scattered flares are singular
and the reflected ghosts are unavailable. Therefore, a comprehensive
deterioration procedure is crucial for constructing the dataset of flare
removal. Based on the theoretical analysis and real-world evaluation, we
propose a well-developed methodology for generating the data-pairs with flare
deterioration. The procedure is comprehensive, where the similarity of
scattered flares and the symmetric effect of reflected ghosts are realized.
Moreover, we also construct a real-shot pipeline that respectively processes
the effects of scattering and reflective flares, aiming to directly generate
the data for end-to-end methods. Experimental results show that the proposed
methodology add diversity to the existing flare datasets and construct a
comprehensive mapping procedure for flare data pairs. And our method facilities
the data-driven model to realize better restoration in flare images and
proposes a better evaluation system based on real shots, resulting promote
progress in the area of real flare removal.

本文提出了一种生成包含散射耀斑和对称反射背景的散斑恶化数据对的方法，可用于提高散斑去除算法的效果和评估方法。