# Multi-scale Dynamic Feature Encoding Network for Image Demoireing

arXiv: https://arxiv.org/abs/1909.11947 · HTML: https://mlsanity.com/paper/1909.11947/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2019.

## Built on by (most cited first, 23 of 23)

- [Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoireing](https://mlsanity.com/paper/2207.09935/) (2022)
- [Image Demoireing in RAW and sRGB Domains](https://mlsanity.com/paper/2312.09063/) (2023)
- [Real-Time Image Demoireing on Mobile Devices](https://mlsanity.com/paper/2302.02184/) (2023)
- [FC3DNet: A Fully Connected Encoder-Decoder for Efficient Demoir'eing](https://mlsanity.com/paper/2406.14912/) (2024)
- [Direction-aware Video Demoireing with Temporal-guided Bilateral Learning](https://mlsanity.com/paper/2308.13388/) (2023)
- [MixNet: Efficient Global Modeling for Ultra-High-Definition Image Restoration](https://mlsanity.com/paper/2401.10666/) (2024)
- [Moir'e Attack (MA): A New Potential Risk of Screen Photos](https://mlsanity.com/paper/2110.10444/) (2021)
- [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)
- [Video Demoireing using Focused-Defocused Dual-Camera System](https://mlsanity.com/paper/2508.03449/) (2025)
- [Fast and Light-Weight Network for Single Frame Structured Illumination Microscopy Super-Resolution](https://mlsanity.com/paper/2111.09103/) (2021)
- [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)
- [AADNet: Attention aware Demoir'eing Network](https://mlsanity.com/paper/2403.08384/) (2024)
- [ShapeMoir'e: Channel-Wise Shape-Guided Network for Image Demoir'eing](https://mlsanity.com/paper/2404.18155/) (2024)
- [SIDME: Self-supervised Image Demoir'eing via Masked Encoder-Decoder Reconstruction](https://mlsanity.com/paper/2504.12245/) (2025)
- [Freqformer: Image-Demoir'eing Transformer via Effective Frequency Decomposition](https://mlsanity.com/paper/2505.19120/) (2025)
- [Moir'eNet: A Compact Dual-Domain Network for Image Demoir'eing](https://mlsanity.com/paper/2509.18910/) (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)
- [Hidden-Shot: Towards One-Shot Task Generalization for Low-Level Vision Generalist Models](https://mlsanity.com/paper/2607.01535/) (2026)

## Abstract (arXiv)

The prevalence of digital sensors, such as digital cameras and mobile phones, simplifies the acquisition of photos. Digital sensors, however, suffer from producing Moire when photographing objects having complex textures, which deteriorates the quality of photos. Moire spreads across various frequency bands of images and is a dynamic texture with varying colors and shapes, which pose two main challenges in demoireing---an important task in image restoration. In this paper, towards addressing the first challenge, we design a multi-scale network to process images at different spatial resolutions, obtaining features in different frequency bands, and thus our method can jointly remove moire in different frequency bands. Towards solving the second challenge, we propose a dynamic feature encoding module (DFE), embedded in each scale, for dynamic texture. Moire pattern can be eliminated more effectively via DFE.Our proposed method, termed Multi-scale convolutional network with Dynamic feature encoding for image DeMoireing (MDDM), can outperform the state of the arts in fidelity as well as perceptual on benchmarks.