Image demoiréing aims to remove structured moiré artifacts in recaptured imagery, where degradations are highly frequency-dependent and vary across scales and directions. While recent deep networks achieve high-quality restoration, their full-precision designs remain costly for deployment. Binarization offers an extreme compression regime by quantizing both activations and weights to 1-bit. Yet, it has been rarely studied for demoiréing and performs poorly when naively applied. In this work, we propose BinaryDemoire, a binarized demoiréing framework that explicitly accommodates the frequency structure of moiré degradations. First, we introduce a moiré-aware binary gate (MABG) that extracts lightweight frequency descriptors together with activation statistics. It predicts channel-wise gating coefficients to condition the aggregation of binary convolution responses. Second, we design a shuffle-grouped residual adapter (SGRA) that performs structured sparse shortcut alignment. It further integrates interleaved mixing to promote information exchange across different channel partitions. Extensive experiments on four benchmarks demonstrate that the proposed BinaryDemoire surpasses current binarization methods. Code: https://github.com/zhengchen1999/BinaryDemoire.

翻译：图像去摩尔纹旨在消除重拍摄图像中的结构化摩尔纹伪影，这些退化效应具有高度频率依赖性，且在不同尺度和方向上呈现显著差异。尽管近期深度网络已能实现高质量复原，但其全精度设计在部署时仍存在较高计算成本。二值化通过将激活值和权重量化为1比特，提供了一种极端的压缩方案。然而，该方法在去摩尔纹任务中研究甚少，且直接应用时性能欠佳。本研究提出BinaryDemoire——一个显式适应摩尔纹退化频率结构的二值化去摩尔纹框架。首先，我们引入摩尔纹感知二值门控模块，该模块通过提取轻量级频率描述符并结合激活统计量，预测通道级门控系数以调节二值卷积响应的聚合过程。其次，我们设计了分组洗牌残差适配器，该模块通过结构化稀疏捷径对齐，并进一步集成交错混合机制以促进不同通道分区间的信息交换。在四个基准数据集上的大量实验表明，所提出的BinaryDemoire方法超越了现有二值化技术的性能。代码：https://github.com/zhengchen1999/BinaryDemoire。