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

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🧭 Overview

Quick links: Motivation | Architecture | Results | Training | Citation

📖 Introduction

Flicker artifacts are caused by unstable illumination and row-wise exposure under the rolling-shutter mechanism, leading to structured spatial-temporal degradation. Unlike common degradations such as noise or low-light, flicker exhibits two intrinsic properties: periodicity and directionality.
Flickerformer is a transformer-based framework for burst flicker removal, built on three key components:

• PFM (Phase-based Fusion Module): adaptively fuses burst features via inter-frame phase correlation;
• AFFN (Autocorrelation Feed-Forward Network): captures intra-frame periodic structures through autocorrelation;
• WDAM (Wavelet-based Directional Attention Module): uses directional high-frequency wavelet cues to guide low-frequency dark-region restoration.

The model suppresses flicker effectively while reducing ghosting artifacts, and achieves superior quantitative and visual performance compared with prior methods.

💡 Motivation

Flicker is not random noise. It is a structured degradation with explicit physical priors. As shown below, phase information is strongly related to flicker spatial distribution, and the rolling-shutter mechanism introduces directional stripe patterns.

🧠 Flickerformer Architecture

Flickerformer adopts a U-shaped encoder-decoder design and explicitly embeds periodicity and directionality priors:

• PFM + AFFN: periodicity-aware modeling in the frequency domain (inter-frame and intra-frame);
• WDAM: directionality-aware modeling in the spatial-wavelet domain (high-frequency guidance for low-frequency restoration).

🖼️ Qualitative Results

Across diverse flicker scenarios, Flickerformer localizes affected regions more precisely, restores illumination consistency, and preserves texture and color fidelity.

⚙️ Installation

1. Install dependencies

cd Flickerformer
pip install -r requirements.txt

2. Install basicsr in the project root

python setup.py develop

📦 Dataset

BurstDeflicker: Kaggle Link

Recommended dataset structure:

dataset/
├── BurstFlicker-G
│   ├── train
│   │   ├── input
│   │   └── gt
│   └── test
└── BurstFlicker-S
    ├── train
    │   ├── input
    │   │   ├── 0001
    │   │   │   ├── 0001.png
    │   │   │   ├── 0002.png
    │   │   │   └── ...
    │   │   └── ...
    │   └── gt
    │       ├── 0001
    │       └── ...
    └── test

To convert mp4 videos into frames:

cd dataset
python cut.py

🚀 Training

bash ./dist_train.sh 2 options/Flickerformer.yml

✅ Testing and Evaluation

python test.py --input dataset/BurstFlicker-S/test-resize/input --output result/flickerformer --model_path Flickerformer.pth

python evaluate.py --input result/

📚 Citation

If you find this project useful, please cite:

@inproceedings{qu2026flickerformer,
  title={It Takes Two: A Duet of Periodicity and Directionality for Burst Flicker Removal},
  author={Qu, Lishen and Zhou, Shihao and Liang, Jie and Zeng, Hui and Zhang, Lei and Yang, Jufeng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2026}
}