《雙重奏：週期性與方向性協同消除突發閃爍》

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

March 24, 2026

作者: Lishen Qu, Shihao Zhou, Jie Liang, Hui Zeng, Lei Zhang, Jufeng Yang

cs.AI

摘要

由光照不稳定和逐行曝光不一致产生的闪烁伪影，是短曝光摄影中的重大挑战，会严重降低图像质量。与典型伪影（如噪声和低光照）不同，闪烁是一种具有特定时空模式的结构化退化，现有通用修复框架未能充分考虑其特性，导致闪烁抑制效果欠佳并产生重影伪影。本研究揭示了闪烁伪影具有周期性和方向性两个本质特征，据此提出基于Transformer架构的Flickerformer模型，可在消除闪烁的同时避免引入重影。该模型包含三个核心组件：基于相位的融合模块（PFM）、自相关前馈网络（AFFN）和小波方向注意力模块（WDAM）。PFM基于周期性特征执行帧间相位关联，自适应聚合连拍图像特征；AFFN通过自相关挖掘帧内结构规律性，共同增强网络感知空间重复模式的能力。此外，针对闪烁伪影的方向性特征，WDAM利用小波域的高频变化指导低频暗区修复，实现闪烁伪影的精准定位。大量实验表明，Flickerformer在定量指标和视觉质量上均优于现有最优方法。源代码已发布于https://github.com/qulishen/Flickerformer。

English

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 https://github.com/qulishen/Flickerformer.