# Image Demoir'eing Using Dual Camera Fusion on Mobile Phones

arXiv: https://arxiv.org/abs/2506.08361 · HTML: https://mlsanity.com/paper/2506.08361/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 32 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 21 of 21)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [LightGlue: Local Feature Matching at Light Speed](https://mlsanity.com/paper/2306.13643/) (2023)
- [FlowFormer: A Transformer Architecture for Optical Flow](https://mlsanity.com/paper/2203.16194/) (2022)
- [GlueStick: Robust Image Matching by Sticking Points and Lines Together](https://mlsanity.com/paper/2304.02008/) (2023)
- [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)
- [TransXNet: Learning Both Global and Local Dynamics with a Dual Dynamic Token Mixer for Visual Recognition](https://mlsanity.com/paper/2310.19380/) (2023)
- [Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations](https://mlsanity.com/paper/2203.01325/) (2022)
- [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)
- [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)
- [Real-Time Image Demoireing on Mobile Devices](https://mlsanity.com/paper/2302.02184/) (2023)
- [Learning Image Demoireing from Unpaired Real Data](https://mlsanity.com/paper/2401.02719/) (2024)
- [Self-Supervised Learning for Real-World Super-Resolution from Dual and Multiple Zoomed Observations](https://mlsanity.com/paper/2405.02171/) (2024)

## Built on by (most cited first, 4 of 4)

- [RIFLE: Removal of Image Flicker-Banding via Latent Diffusion Enhancement](https://mlsanity.com/paper/2509.24644/) (2025)
- [Combined Flicker-banding and Moire Removal for Screen-Captured Images](https://mlsanity.com/paper/2602.01559/) (2026)
- [Improving Complex Moir'e Removal with Generative Supervision](https://mlsanity.com/paper/2608.17883/) (2026)
- [DuSRFlow: Dual-Lens Super-Resolution via Parallax-Aware Flow Estimation and Complementary Reference Warping](https://mlsanity.com/paper/2610.14630/) (2026)

## Abstract (arXiv)

When shooting electronic screens, moir'e patterns usually appear in captured images, which seriously affects the image quality. Existing image demoir'eing methods face great challenges in removing large and heavy moir'e. To address the issue, we propose to utilize Dual Camera fusion for Image Demoir'eing (DCID), ie, using the ultra-wide-angle (UW) image to assist the moir'e removal of wide-angle (W) image. This is inspired by two motivations: (1) the two lenses are commonly equipped with modern smartphones, (2) the UW image generally can provide normal colors and textures when moir'e exists in the W image mainly due to their different focal lengths. In particular, we propose an efficient DCID method, where a lightweight UW image encoder is integrated into an existing demoir'eing network and a fast two-stage image alignment manner is present. Moreover, we construct a large-scale real-world dataset with diverse mobile phones and monitors, containing about 9,000 samples. Experiments on the dataset show our method performs better than state-of-the-art methods. Code and dataset are available at https://github.com/Mrduckk/DCID.