AF2CN: Towards effective demoiréing from multi-resolution images

Abstract

Recently, CNN-based methods have gained significant attention for addressing the demoiré task due to their powerful feature extraction capabilities. However, these methods are generally trained on datasets with fixed resolutions, limiting their applicability to diverse real-world scenarios. To address this limitation, we introduce a more generalized task: effective demoiréing across multiple resolutions. To facilitate this task, we constructed MTADM, the first multi-resolution moiré dataset, designed to capture diverse real-world scenarios. Leveraging this dataset, we conducted extensive studies and introduced the Adaptive Fractional Calculus and Adjacency Fusion Convolution Network (AF2CN). Specifically, we employ fractional derivatives to develop an adaptive frequency enhancement module, which refines spatial distribution and texture details in moiré patterns. Additionally, we design a spatial attention gate to enhance deep feature interaction. Extensive experiments demonstrate that AF2CN effectively handles multi-resolution moiré patterns. It significantly outperforms previous state-of-the-art methods on fixed-resolution benchmarks while requiring fewer parameters and achieving lower computational costs.