In recent years, convolutional neural networks have excelled in image Moiré pattern removal, yet their high memory consumption poses challenges for resource-constrained devices. To address this, we propose the lightweight multi-scale network (LMSNet). Designing lightweight multi-scale feature extraction blocks and efficient adaptive channel fusion modules, we extend the receptive field of feature extraction and introduce lightweight convolutional decomposition. LMSNet achieves a balance between parameter numbers and reconstruction performance. Extensive experiments demonstrate that our LMSNet, with 0.77 million parameters, achieves Moiré pattern removal performance comparable to full high definition demoiréing network ( |
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Feature extraction
Image processing
Convolution
Performance modeling
Design
Education and training
Image quality