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arxiv: 2306.15884 · v1 · pith:EWDLJII3new · submitted 2023-06-28 · 💻 cs.CV

Toward Real Flare Removal: A Comprehensive Pipeline and A New Benchmark

classification 💻 cs.CV
keywords flarecomprehensiveremovaldeteriorationflaresghostsprocedurereal
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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.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Semi-LAR: Semi-supervised Contrastive Learning with Linear Attention for Removal of Nighttime Flares

    cs.CV 2026-05 unverdicted novelty 5.0

    Semi-LAR is a semi-supervised contrastive learning framework with linear attention for nighttime flare removal that refines pseudo-labels via quality assessment and uses flare-aware patch-level contrastive losses.