# Image Demoireing in RAW and sRGB Domains

arXiv: https://arxiv.org/abs/2312.09063 · HTML: https://mlsanity.com/paper/2312.09063/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 14 Dec 2023.

## Builds on (most cited first, 17 of 17)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Wavelet-Based Dual-Branch Network for Image Demoireing](https://mlsanity.com/paper/2007.07173/) (2020)
- [Image Demoireing with Learnable Bandpass Filters](https://mlsanity.com/paper/2004.00406/) (2020)
- [Abandoning the Bayer-Filter to See in the Dark](https://mlsanity.com/paper/2203.04042/) (2022)
- [Robust Reflection Removal with Reflection-free Flash-only Cues](https://mlsanity.com/paper/2103.04273/) (2021)
- [Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks](https://mlsanity.com/paper/1803.05215/) (2018)
- [Moir'e Photo Restoration Using Multiresolution Convolutional Neural Networks](https://mlsanity.com/paper/1805.02996/) (2018)
- [Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoireing](https://mlsanity.com/paper/2207.09935/) (2022)
- [Towards Low Light Enhancement with RAW Images](https://mlsanity.com/paper/2112.14022/) (2021)
- [Video Demoireing with Relation-Based Temporal Consistency](https://mlsanity.com/paper/2204.02957/) (2022)
- [Multi-scale Dynamic Feature Encoding Network for Image Demoireing](https://mlsanity.com/paper/1909.11947/) (2019)
- [Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Dataset](https://mlsanity.com/paper/2209.12475/) (2022)
- [Recaptured Raw Screen Image and Video Demoir'eing via Channel and Spatial Modulations](https://mlsanity.com/paper/2310.20332/) (2023)
- [Direction-aware Video Demoireing with Temporal-guided Bilateral Learning](https://mlsanity.com/paper/2308.13388/) (2023)
- [FPANet: Frequency-based Video Demoireing using Frame-level Post Alignment](https://mlsanity.com/paper/2301.07330/) (2023)

## Built on by (most cited first, 15 of 15)

- [Image Demoir'eing Using Dual Camera Fusion on Mobile Phones](https://mlsanity.com/paper/2506.08361/) (2025)
- [RIFLE: Removal of Image Flicker-Banding via Latent Diffusion Enhancement](https://mlsanity.com/paper/2509.24644/) (2025)
- [FAPE-IR: Frequency-Aware Planning and Execution Framework for All-in-One Image Restoration](https://mlsanity.com/paper/2511.14099/) (2025)
- [Alignment-free Raw Video Demoireing](https://mlsanity.com/paper/2408.10679/) (2024)
- [DSDNet: Raw Domain Demoir'eing via Dual Color-Space Synergy](https://mlsanity.com/paper/2504.15756/) (2025)
- [Combined Flicker-banding and Moire Removal for Screen-Captured Images](https://mlsanity.com/paper/2602.01559/) (2026)
- [Moir'eXNet: Adaptive Multi-Scale Demoir'eing with Linear Attention Test-Time Training and Truncated Flow Matching Prior](https://mlsanity.com/paper/2506.15929/) (2025)
- [Moir'e Zero: An Efficient and High-Performance Neural Architecture for Moir'e Removal](https://mlsanity.com/paper/2507.22407/) (2025)
- [MoCHA-former: Moir'e-Conditioned Hybrid Adaptive Transformer for Video Demoir'eing](https://mlsanity.com/paper/2508.14423/) (2025)
- [QuantDemoire: Quantization with Outlier Aware for Image Demoir'eing](https://mlsanity.com/paper/2510.04066/) (2025)
- [ShapeMoir'e: Channel-Wise Shape-Guided Network for Image Demoir'eing](https://mlsanity.com/paper/2404.18155/) (2024)
- [Freqformer: Image-Demoir'eing Transformer via Effective Frequency Decomposition](https://mlsanity.com/paper/2505.19120/) (2025)
- [BinaryDemoire: Moir'e-Aware Binarization for Image Demoir'eing](https://mlsanity.com/paper/2602.03176/) (2026)
- [Fabric Image Demoir'eing Benchmark from Synthesis to Restoration](https://mlsanity.com/paper/2606.24072/) (2026)
- [Bricker to BRACE: A Bracket Exposure RAW Dataset and Restoration Model for Flicker-Banding](https://mlsanity.com/paper/2606.29845/) (2026)

## Abstract (arXiv)

Moire patterns frequently appear when capturing screens with smartphones or cameras, potentially compromising image quality. Previous studies suggest that moire pattern elimination in the RAW domain offers greater effectiveness compared to demoireing in the sRGB domain. Nevertheless, relying solely on RAW data for image demoireing is insufficient in mitigating the color cast due to the absence of essential information required for the color correction by the image signal processor (ISP). In this paper, we propose to jointly utilize both RAW and sRGB data for image demoireing (RRID), which are readily accessible in modern smartphones and DSLR cameras. We develop Skip-Connection-based Demoireing Module (SCDM) with Gated Feedback Module (GFM) and Frequency Selection Module (FSM) embedded in skip-connections for the efficient and effective demoireing of RAW and sRGB features, respectively. Subsequently, we design a RGB Guided ISP (RGISP) to learn a device-dependent ISP, assisting the process of color recovery. Extensive experiments demonstrate that our RRID outperforms state-of-the-art approaches, in terms of the performance in moire pattern removal and color cast correction by 0.62dB in PSNR and 0.003 in SSIM.