Eliminating Moiré Patterns Across Diverse Image Resolutions via DMMNet
Yikun Ma, Haoran Qi, Zhi Juan Jin · IEEE Transactions on Multimedia · 2025
The occurrence of frequency aliasing between the camera and high-frequency scene elements causes moiré patterns in images, leading to color distortions and a loss of fine details, thereby reducing image quality. The intricate frequency characteristics and diverse appearances inherent in moiré patterns render their removal, commonly referred to as demoiréing, particularly challenging. Recent advancements in deep learning-based demoiréing methods have showcased notable efficacy. However, prevailing techniques often specialize in mitigating moiré patterns exclusively within either the frequency or spatial domains. Additionally, these methods generally perform well at specific image resolutions, but struggle to maintain effectiveness across different resolutions due to less generalized architectures. To address these issues, we propose a Dual-domain Multi-level Multi-scale Network DMMNet, working in both spatial and frequency domains sequentially. The Multi-scale Multi-level Demoire Stage (MMDS) in our framework focuses on moiré patterns removal in the spatial domain. To adeptly integrate features from various semantic levels, we introduce a pioneering plug-and-play Adjacent Cross Attention (ACA) module within the MMDS. Subsequently, the Frequency Separation and Reconstruction Stage (FSRS) restores high-frequency texture details, reconstructs color information, and eliminates residual moiré patterns in the wavelet frequency domain. Ultimately, the clean image is obtained by converting it back to the spatial domain. Extensive experimental assessments, spanning both quantitative metrics and qualitative visual evaluations, attest to the superior efficacy of DMMNet to State-Of-The-Art (SOTA) demoiréing methods, concurrently exhibiting enhanced generalization for demoiréing across diverse image resolutions. We posit that the proposed methodology presents a viable solution for broader applications in the realm of demoiréing. Code will be available onhttps://github.com/Mr-Ma-yikun/DMMNet.