Freqformer: Image-Demoiréing Transformer via Effective Frequency Decomposition

Image demoiréing remains a challenging task due to the com-plex interplay between texture corruption and color distortions causedby moiré patterns. Existing methods, especially those relying on directimage-to-image restoration, often fail to disentangle these intertwinedartifacts effectively. While frequency-aware approaches offer a promisingdirection, their potential is hindered by the discrete transform (e.g., Haarwavelet or block-based DCT), which may suffer from spatial discontinu-ity, channel redundancy, and further cause error accumulation duringtheir fixed inverse processes. In this paper, we present Freqformer, aTransformer-based framework specifically designed for image demoiréingthrough targeted frequency separation. Our method performs an effectivefrequency decomposition that splits moiré patterns into high-frequencyspatially-localized textures and low-frequency scale-robust color distor-tions, which are then handled by a dual-branch architecture and anasymmetric training scheme tailored to their distinct characteristics. Wefurther propose a learnable Frequency Composition Transform (FCT)module to adaptively fuse the frequency-specific outputs, enabling con-sistent and high-fidelity reconstruction. To better aggregate the spatialdependencies and the inter-channel complementary information, we in-troduce a Spatial-Aware Channel Attention (SA-CA) module that refinesmoiré-sensitive regions without incurring high computational cost. Ex-tensive experiments on various demoiréing benchmarks demonstrate thatFreqformer achieves state-of-the-art performance with a compact modelsize. The code will be made publicly available at https://github.com/xyLiu339/Freqformer.