Fabric Image Demoir\'eing Benchmark from Synthesis to Restoration

Fabric moir\'e 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 moir\'e, which stems from strictly periodic display lattices, fabric moir\'e 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 demoir\'eing substantially more ill-posed. Consequently, existing models trained on screen moir\'e datasets generalize poorly to these complex textile patterns. Despite its practical importance, fabric image demoir\'eing remains underexplored and lacks standardized benchmarks. We present the first comprehensive benchmark for fabric image demoir\'eing. 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 demoir\'eing.