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

TL;DR

This paper introduces a dual camera fusion approach for mobile phone image demoiréing, leveraging ultra-wide-angle images to improve removal of moiré patterns caused by electronic screens.

Contribution

It proposes a novel dual camera fusion method that integrates a lightweight encoder and a two-stage alignment, and provides a large real-world dataset for moiré removal.

Findings

Outperforms state-of-the-art demoiréing methods on the new dataset.

Effectively removes large and heavy moiré patterns.

Utilizes dual camera data to enhance image quality.

Abstract

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.