首个布料摩尔纹去噪基准，解决真实数据难获取难题
Fabric Image Demoiréing Benchmark from Synthesis to Restoration
- 基于物理模型合成多分辨率布料图像对，可控摩尔纹强度
- 构建16,050张配对图像数据集，覆盖不同织物纹理和采样条件
- 提出通用基线模型，为后续研究提供可复现评估平台
布料摩尔纹是由细密纺织图案与相机传感器网格相互作用引起的采样混叠伪影，产生结构化干扰严重降低图像质量。与源于严格周期显示阵列的屏幕摩尔纹不同，布料摩尔纹因织物编织的宽带半周期特性而更具挑战性。固有纹理与混叠成分间存在严重频谱重叠，使去摩尔纹问题更病态。现有在屏幕摩尔纹数据集上训练的模型在复杂织物图案上泛化能力差。尽管应用价值高，布料图像去摩尔纹仍缺乏标准基准。本文首次提出综合性布料图像去摩尔纹基准。为克服真实像素级成对数据获取困难，开发了基于物理动机的合成框架，构建包含16,050对多分辨率布料图像的大规模数据集，且可控制混叠严重程度。此外，定制了一种基线模型，在该基准上表现出良好性能与强泛化能力。本基准为推进布料图像去摩尔纹研究提供标准化平台。
原文摘要 · Abstract (English)
Fabric moiré is a sampling-induced aliasing artifact caused by the interaction between fine textile patterns and camera sensor grids, producing structured interference that severely degrades image quality. Unlike screen-induced moiré, which stems from strictly periodic display lattices, fabric moiré is intrinsically more challenging due to the broadband and semi-periodic nature of textile weaves. The heavy spectral overlap between intrinsic texture and aliasing components renders fabric demoiréing substantially more ill-posed. Consequently, existing models trained on screen moiré datasets generalize poorly to these complex textile patterns. Despite its practical importance, fabric image demoiréing remains underexplored and lacks standardized benchmarks. We present the first comprehensive benchmark for fabric image demoiréing. To address the difficulty of acquiring pixel-aligned real-world pairs, we develop a physically motivated synthesis framework and construct a large-scale dataset comprising 16,050 paired multi-resolution fabric images with controllable aliasing severity. Furthermore, we customize a baseline model, which establishes promising performance on the proposed benchmark dataset with strong generalization ability. Our benchmark provides a standardized platform for advancing research in fabric image demoiréing.
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