TY - GEN
T1 - Image Demoiréing Using Dual Camera Fusion on Mobile Phones
AU - Mei, Yanting
AU - Zhang, Zhilu
AU - Wu, Xiaohe
AU - Zuo, Wangmeng
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - When shooting electronic screens, moiré patterns usually appear in captured images, which seriously affects the image quality. Existing image demoiréing methods face great challenges in removing large and heavy moiré. To address the issue, we propose to utilize Dual Camera fusion for Image Demoiréing (DCID), i.e., using the ultra-wide-angle (UW) image to assist the moiré 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 moiré 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 demoiréing 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.
AB - When shooting electronic screens, moiré patterns usually appear in captured images, which seriously affects the image quality. Existing image demoiréing methods face great challenges in removing large and heavy moiré. To address the issue, we propose to utilize Dual Camera fusion for Image Demoiréing (DCID), i.e., using the ultra-wide-angle (UW) image to assist the moiré 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 moiré 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 demoiréing 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.
KW - Dual Camera Fusion
KW - Image Demoiréing
KW - Mobile Phones
UR - https://www.scopus.com/pages/publications/105022638741
U2 - 10.1109/ICME59968.2025.11209149
DO - 10.1109/ICME59968.2025.11209149
M3 - 会议稿件
AN - SCOPUS:105022638741
T3 - Proceedings - IEEE International Conference on Multimedia and Expo
BT - 2025 IEEE International Conference on Multimedia and Expo
PB - IEEE Computer Society
T2 - 2025 IEEE International Conference on Multimedia and Expo, ICME 2025
Y2 - 30 June 2025 through 4 July 2025
ER -