Freqformer: Image-Demoir\'eing Transformer via Efficient Frequency Decomposition

TL;DR

Freqformer introduces a frequency decomposition-based Transformer framework for image demoiréing, effectively separating textures and color distortions, leading to state-of-the-art results with a compact model.

Contribution

The paper proposes a novel frequency-aware Transformer architecture with a learnable frequency composition module and spatial-aware channel attention for improved demoiréing.

Findings

Achieves state-of-the-art performance on demoiréing benchmarks.

Employs a dual-branch architecture for frequency-specific processing.

Demonstrates high fidelity reconstruction with a compact model.

Abstract

Image demoir\'eing remains a challenging task due to the complex interplay between texture corruption and color distortions caused by moir\'e patterns. Existing methods, especially those relying on direct image-to-image restoration, often fail to disentangle these intertwined artifacts effectively. While wavelet-based frequency-aware approaches offer a promising direction, their potential remains underexplored. In this paper, we present Freqformer, a Transformer-based framework specifically designed for image demoir\'eing through targeted frequency separation. Our method performs an effective frequency decomposition that explicitly splits moir\'e patterns into high-frequency spatially-localized textures and low-frequency scale-robust color distortions, which are then handled by a dual-branch architecture tailored to their distinct characteristics. We further propose a learnable Frequency Composition Transform (FCT) module to adaptively fuse the frequency-specific outputs, enabling consistent and high-fidelity reconstruction. To better aggregate the spatial dependencies and the inter-channel complementary information, we introduce a Spatial-Aware Channel Attention (SA-CA) module that refines moir\'e-sensitive regions without incurring high computational cost. Extensive experiments on various demoir\'eing benchmarks demonstrate that Freqformer achieves state-of-the-art performance with a compact model size. The code is publicly available at https://github.com/xyLiu339/Freqformer.