# Self-Adaptively Learning to Demoire from Focused and Defocused Image Pairs

arXiv: https://arxiv.org/abs/2011.02055 · HTML: https://mlsanity.com/paper/2011.02055/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 14 of 14)

- [Low-Light Video Enhancement with Synthetic Event Guidance](https://mlsanity.com/paper/2208.11014/) (2022)
- [Gradient-based Bi-level Optimization for Deep Learning: A Survey](https://mlsanity.com/paper/2207.11719/) (2022)
- [Video Demoireing with Relation-Based Temporal Consistency](https://mlsanity.com/paper/2204.02957/) (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)
- [SiamTrans: Zero-Shot Multi-Frame Image Restoration with Pre-Trained Siamese Transformers](https://mlsanity.com/paper/2112.09426/) (2021)
- [FPANet: Frequency-based Video Demoireing using Frame-level Post Alignment](https://mlsanity.com/paper/2301.07330/) (2023)
- [Image Demoir'eing Using Dual Camera Fusion on Mobile Phones](https://mlsanity.com/paper/2506.08361/) (2025)
- [Alignment-free Raw Video Demoireing](https://mlsanity.com/paper/2408.10679/) (2024)
- [Video Demoireing using Focused-Defocused Dual-Camera System](https://mlsanity.com/paper/2508.03449/) (2025)
- [Combined Flicker-banding and Moire Removal for Screen-Captured Images](https://mlsanity.com/paper/2602.01559/) (2026)
- [MoCHA-former: Moir'e-Conditioned Hybrid Adaptive Transformer for Video Demoir'eing](https://mlsanity.com/paper/2508.14423/) (2025)
- [SIDME: Self-supervised Image Demoir'eing via Masked Encoder-Decoder Reconstruction](https://mlsanity.com/paper/2504.12245/) (2025)
- [Fabric Image Demoir'eing Benchmark from Synthesis to Restoration](https://mlsanity.com/paper/2606.24072/) (2026)

## Abstract (arXiv)

Moire artifacts are common in digital photography, resulting from the interference between high-frequency scene content and the color filter array of the camera. Existing deep learning-based demoireing methods trained on large scale datasets are limited in handling various complex moire patterns, and mainly focus on demoireing of photos taken of digital displays. Moreover, obtaining moire-free ground-truth in natural scenes is difficult but needed for training. In this paper, we propose a self-adaptive learning method for demoireing a high-frequency image, with the help of an additional defocused moire-free blur image. Given an image degraded with moire artifacts and a moire-free blur image, our network predicts a moire-free clean image and a blur kernel with a self-adaptive strategy that does not require an explicit training stage, instead performing test-time adaptation. Our model has two sub-networks and works iteratively. During each iteration, one sub-network takes the moire image as input, removing moire patterns and restoring image details, and the other sub-network estimates the blur kernel from the blur image. The two sub-networks are jointly optimized. Extensive experiments demonstrate that our method outperforms state-of-the-art methods and can produce high-quality demoired results. It can generalize well to the task of removing moire artifacts caused by display screens. In addition, we build a new moire dataset, including images with screen and texture moire artifacts. As far as we know, this is the first dataset with real texture moire patterns.