# Bricker to BRACE: A Bracket Exposure RAW Dataset and Restoration Model for Flicker-Banding

arXiv: https://arxiv.org/abs/2606.29845 · HTML: https://mlsanity.com/paper/2606.29845/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 52 references that are in the map.
- First posted on arXiv 29 Jun 2026.

## Builds on (most cited first, 30 of 31)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Learning to See in the Dark](https://mlsanity.com/paper/1805.01934/) (2018)
- [PubLayNet: largest dataset ever for document layout analysis](https://mlsanity.com/paper/1908.07836/) (2019)
- [Burst Denoising with Kernel Prediction Networks](https://mlsanity.com/paper/1712.02327/) (2017)
- [DocBank: A Benchmark Dataset for Document Layout Analysis](https://mlsanity.com/paper/2006.01038/) (2020)
- [Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approach](https://mlsanity.com/paper/2412.03017/) (2024)
- [Deep Burst Super-Resolution](https://mlsanity.com/paper/2101.10997/) (2021)
- [Burst Image Restoration and Enhancement](https://mlsanity.com/paper/2110.03680/) (2021)
- [Burstormer: Burst Image Restoration and Enhancement Transformer](https://mlsanity.com/paper/2304.01194/) (2023)
- [HDRFlow: Real-Time HDR Video Reconstruction with Large Motions](https://mlsanity.com/paper/2403.03447/) (2024)
- [Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation](https://mlsanity.com/paper/1904.12945/) (2019)
- [Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolution](https://mlsanity.com/paper/2212.05342/) (2022)
- [Low-light Image Restoration with Short- and Long-exposure Raw Pairs](https://mlsanity.com/paper/2007.00199/) (2020)
- [Self-Supervised Image Restoration with Blurry and Noisy Pairs](https://mlsanity.com/paper/2211.07317/) (2022)
- [Image Demoireing in RAW and sRGB Domains](https://mlsanity.com/paper/2312.09063/) (2023)
- [Multiscale Sliced Wasserstein Distances as Perceptual Color Difference Measures](https://mlsanity.com/paper/2407.10181/) (2024)
- [NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Results](https://mlsanity.com/paper/2505.12089/) (2025)
- [VideoGameBunny: Towards vision assistants for video games](https://mlsanity.com/paper/2407.15295/) (2024)
- [Exposure Bracketing Is All You Need For A High-Quality Image](https://mlsanity.com/paper/2401.00766/) (2024)
- [CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement Task](https://mlsanity.com/paper/2404.14132/) (2024)
- [RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution](https://mlsanity.com/paper/2306.17595/) (2023)
- [Human Body Restoration with One-Step Diffusion Model and A New Benchmark](https://mlsanity.com/paper/2502.01411/) (2025)
- [RIFLE: Removal of Image Flicker-Banding via Latent Diffusion Enhancement](https://mlsanity.com/paper/2509.24644/) (2025)
- [BurstDeflicker: A Benchmark Dataset for Flicker Removal in Dynamic Scenes](https://mlsanity.com/paper/2510.09996/) (2025)
- [It Takes Two: A Duet of Periodicity and Directionality for Burst Flicker Removal](https://mlsanity.com/paper/2603.22794/) (2026)
- [Efficient HDR Reconstruction from Real-World Raw Images](https://mlsanity.com/paper/2306.10311/) (2023)
- [Improving Bracket Image Restoration and Enhancement with Flow-guided Alignment and Enhanced Feature Aggregation](https://mlsanity.com/paper/2404.10358/) (2024)

## Abstract (arXiv)

Flicker-banding (FB), arises from temporal aliasing between a camera's rolling shutter and a display's brightness modulation, degrading screen-captured image readability with color shifts and jagged patterns. Existing single-frame methods with simplified parametric stripe models cannot reliably distinguish these artifacts from genuine texture. To address this, we conduct a systematic analysis of complex FB morphologies and reveal their significant variation across exposure settings, motivating a multi-frame bracketed RAW restoration paradigm. We construct Bricker, a synthetic-real bracketed RAW dataset built via ray-tracing-based physical simulation and automated multi-exposure capture tool. We further propose BRACE: Bracketed RAW Flicker-Banding Removal, a multi-frame restoration model that utilizes frequency-aware banding prior and a multi-scale spatial cross-attention modulator (MSCAM) for cross-exposure spatial fusion. We also introduce the Stripe Frequency Consistency (SFC) metric to evaluate banding removal. Experiments demonstrate state-of-the-art performance on both synthetic and real benchmarks. Our dataset and code are available at: https://github.com/ZZH-qwq/BRACE.