# SIDME: Self-supervised Image Demoir'eing via Masked Encoder-Decoder Reconstruction

arXiv: https://arxiv.org/abs/2504.12245 · HTML: https://mlsanity.com/paper/2504.12245/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 16 Apr 2025.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [Wavelet-Based Dual-Branch Network for Image Demoireing](https://mlsanity.com/paper/2007.07173/) (2020)
- [Contextual-based Image Inpainting: Infer, Match, and Translate](https://mlsanity.com/paper/1711.08590/) (2017)
- [Image Demoireing with Learnable Bandpass Filters](https://mlsanity.com/paper/2004.00406/) (2020)
- [Moir'e Photo Restoration Using Multiresolution Convolutional Neural Networks](https://mlsanity.com/paper/1805.02996/) (2018)
- [Multi-scale Dynamic Feature Encoding Network for Image Demoireing](https://mlsanity.com/paper/1909.11947/) (2019)
- [Self-Adaptively Learning to Demoire from Focused and Defocused Image Pairs](https://mlsanity.com/paper/2011.02055/) (2020)
- [AIM 2019 Challenge on Image Demoireing: Dataset and Study](https://mlsanity.com/paper/1911.02498/) (2019)
- [NTIRE 2020 Challenge on Image Demoireing: Methods and Results](https://mlsanity.com/paper/2005.03155/) (2020)
- [Learning Image Demoireing from Unpaired Real Data](https://mlsanity.com/paper/2401.02719/) (2024)
- [UniDemoir'e: Towards Universal Image Demoir'eing with Data Generation and Synthesis](https://mlsanity.com/paper/2502.06324/) (2025)
- [Demoir'eing of Camera-Captured Screen Images Using Deep Convolutional Neural Network](https://mlsanity.com/paper/1804.03809/) (2018)
- [Moir'e Attack (MA): A New Potential Risk of Screen Photos](https://mlsanity.com/paper/2110.10444/) (2021)

## Abstract (arXiv)

Moir'e patterns, resulting from aliasing between object light signals and camera sampling frequencies, often degrade image quality during capture. Traditional demoir'eing methods have generally treated images as a whole for processing and training, neglecting the unique signal characteristics of different color channels. Moreover, the randomness and variability of moir'e pattern generation pose challenges to the robustness of existing methods when applied to real-world data. To address these issues, this paper presents SIDME (Self-supervised Image Demoir'eing via Masked Encoder-Decoder Reconstruction), a novel model designed to generate high-quality visual images by effectively processing moir'e patterns. SIDME combines a masked encoder-decoder architecture with self-supervised learning, allowing the model to reconstruct images using the inherent properties of camera sampling frequencies. A key innovation is the random masked image reconstructor, which utilizes an encoder-decoder structure to handle the reconstruction task. Furthermore, since the green channel in camera sampling has a higher sampling frequency compared to red and blue channels, a specialized self-supervised loss function is designed to improve the training efficiency and effectiveness. To ensure the generalization ability of the model, a self-supervised moir'e image generation method has been developed to produce a dataset that closely mimics real-world conditions. Extensive experiments demonstrate that SIDME outperforms existing methods in processing real moir'e pattern data, showing its superior generalization performance and robustness.