Learning to Remove Lens Flare in Event Camera

• URL: http://arxiv.org/abs/2512.09016v1
• Date: Tue, 09 Dec 2025 18:59:57 GMT
• Title: Learning to Remove Lens Flare in Event Camera
• Authors: Haiqian Han, Lingdong Kong, Jianing Li, Ao Liang, Chengtao Zhu, Jiacheng Lyu, Lai Xing Ng, Xiangyang Ji, Wei Tsang Ooi, Benoit R. Cottereau,
• Abstract summary: We present E-DeflareDeflare, the first framework for removing lens flare from event camera data.<n>We first establish the theoretical foundation by deriving a physics-grounded forward model of the non-linear suppression mechanism.<n> Empowered by this benchmark, we design E-DeflareNet, which achieves state-of-the-art restoration performance.
• Score: 56.9171469873838
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Event cameras have the potential to revolutionize vision systems with their high temporal resolution and dynamic range, yet they remain susceptible to lens flare, a fundamental optical artifact that causes severe degradation. In event streams, this optical artifact forms a complex, spatio-temporal distortion that has been largely overlooked. We present E-Deflare, the first systematic framework for removing lens flare from event camera data. We first establish the theoretical foundation by deriving a physics-grounded forward model of the non-linear suppression mechanism. This insight enables the creation of the E-Deflare Benchmark, a comprehensive resource featuring a large-scale simulated training set, E-Flare-2.7K, and the first-ever paired real-world test set, E-Flare-R, captured by our novel optical system. Empowered by this benchmark, we design E-DeflareNet, which achieves state-of-the-art restoration performance. Extensive experiments validate our approach and demonstrate clear benefits for downstream tasks. Code and datasets are publicly available.

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