DemoiréingMamba: visual state space model for image demoiréing
Chia-Hung Yeh, Chen Lo · 2025
Moiré effect is important in image processing because it causes unwanted patterns that reduce image quality, especially during scanning or digitization. Many deep learning-based approaches, including CNN- and Transformer-based methods, have been effectively employed to remove moiré patterns, delivering promising results. While CNNs struggle with modeling remote dependencies, transformers face challenges due to their quadratic computational complexity. Recently, the state space model (SSM) known as Mamba has emerged as a promising solution, efficiently capturing long-range interactions with the advantage of linear computational complexity. This paper proposes a two-stage moiré removal network through Mamba architecture for removing moiré patterns. In the first stage, we leverage Mamba’s capability to identify moiré-contaminated areas and analyze the spatial distribution of the contamination. In the second stage, the detected patterns, along with the contaminated image, are input into a refinement network for restoration. This distinct separation between detection and refinement enables a more precise and efficient removal of moiré patterns, leading to improved restoration outcomes. Experiments conducted on publicly available datasets demonstrate that our model outperforms state-of-the-art methods, achieving superior quantitative and qualitative results, and producing image restorations with enhanced clarity and fine detail.