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It Takes Two: A Duet of Periodicity and Directionality for Burst Flicker Removal

2026-03-24 04:32:16
Lishen Qu, Shihao Zhou, Jie Liang, Hui Zeng, Lei Zhang, Jufeng Yang

Abstract

Flicker artifacts, arising from unstable illumination and row-wise exposure inconsistencies, pose a significant challenge in short-exposure photography, severely degrading image quality. Unlike typical artifacts, e.g., noise and low-light, flicker is a structured degradation with specific spatial-temporal patterns, which are not accounted for in current generic restoration frameworks, leading to suboptimal flicker suppression and ghosting artifacts. In this work, we reveal that flicker artifacts exhibit two intrinsic characteristics, periodicity and directionality, and propose Flickerformer, a transformer-based architecture that effectively removes flicker without introducing ghosting. Specifically, Flickerformer comprises three key components: a phase-based fusion module (PFM), an autocorrelation feed-forward network (AFFN), and a wavelet-based directional attention module (WDAM). Based on the periodicity, PFM performs inter-frame phase correlation to adaptively aggregate burst features, while AFFN exploits intra-frame structural regularities through autocorrelation, jointly enhancing the network's ability to perceive spatially recurring patterns. Moreover, motivated by the directionality of flicker artifacts, WDAM leverages high-frequency variations in the wavelet domain to guide the restoration of low-frequency dark regions, yielding precise localization of flicker artifacts. Extensive experiments demonstrate that Flickerformer outperforms state-of-the-art approaches in both quantitative metrics and visual quality. The source code is available at this https URL.

Abstract (translated)

闪烁伪影源于光照不稳定与逐行曝光不一致,在短曝光摄影中构成重大挑战,会严重降低图像质量。与噪声、低光照等典型伪影不同,闪烁是一种具有特定时空模式的结构化退化,而当前通用复原框架未将其纳入考量,导致闪烁抑制效果不佳并产生重影伪影。本研究揭示闪烁伪影具有周期性与方向性两大内在特性,并提出基于Transformer的Flickerformer架构,可在消除闪烁的同时避免引入重影。具体而言,Flickerformer包含三个关键组件:基于相位的融合模块(PFM)、自相关前馈网络(AFFN)以及基于小波的方向注意力模块(WDAM)。基于周期性,PFM通过帧间相位相关自适应聚合burst特征;AFFN则利用自相关挖掘帧内结构规律,共同增强网络对空间重复模式的感知能力。此外,受闪烁方向性启发,WDAM借助小波域高频变化引导低频暗区复原,实现闪烁伪影的精确定位。大量实验表明,Flickerformer在定量指标与视觉质量上均优于现有先进方法。源代码已公开于此链接。

URL

https://arxiv.org/abs/2603.22794

PDF

https://arxiv.org/pdf/2603.22794.pdf


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