Toward Flare-Free Images: A Survey

• URL: http://arxiv.org/abs/2310.14354v1
• Date: Sun, 22 Oct 2023 16:46:12 GMT
• Title: Toward Flare-Free Images: A Survey
• Authors: Yousef Kotp, Marwan Torki
• Abstract summary: Lens flare is an artifact that can significantly degrade image quality and affect the performance of computer vision systems. This survey delves into the complex optics of flare formation, arising from factors like internal reflection, scattering, diffraction, and dispersion within the camera lens system. 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.
• Score: 1.0878040851637998
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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