Efficient Image Moir é Removal Algorithm That Integrates Multi-scale Features
1. College of Computer Science, Sichuan Normal University, Chengdu 610101, China
2. College of Physics and Electronic Engineering, Sichuan Normal University, Chengdu 610101, China
| Abstract: | Aiming at the complex and diverse interference of image moir é patterns, the difficulty in fully preserving texture details and edge information, and the poor performance and high computational complexity of existing moir é pattern removal models, a new moir é pattern removal network model based on UNet network is proposed, which integrates weighted convolution and multi-scale attention mechanism. In the feature extraction stage, a weighted convolutional layer based on density parameters is introduced to enhance the central region response of the convolutional kernel through adaptive weight adjustment, while suppressing the influence of edge noise. Secondly, in order to more effectively capture multi-scale feature information, spatial attention blocks and multi-scale channel attention modules are used for deep level feature extraction to obtain more detailed and rich feature representations. In the decoding stage, a combination of skip connections and residual connections is used to enhance the robustness of the model through multi-scale output, and finally, Moir é fringes are removed through weighted convolutional layers. The experimental results show that the evaluation scores of the model on three public datasets, UHDM, TIP, and FHDMI, are better than those of mainstream models for removing image moir é patterns, proving that the model effectively improves the effectiveness of image moir é pattern removal. |
| Keywords: | Image Moir é Removal; Weighted Convolution; Multi-scale Attention; UNet |
| DOI: | 10.57237/j.cst.2026.01.002 |
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Received: 11 December 2025
Accepted: 12 January 2026
Published Online: 20 January 2026
Funding
| 1. | 国家社会科学基金一般项目 (20BMZ092) |
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