Improving Complex Moir'e Removal with Generative Supervision

The availability of high-quality paired data is essential for training learning-based image demoir'eing models. However, it remains challenging for existing datasets to encompass the complex moir'e patterns captured in uncontrolled real-world scenarios. Such degradations typically manifest as large-scale, multicolored moir'e patterns. Moreover, these patterns frequently occur in images for which clean counterparts are difficult to obtain, such as photographs acquired from public displays or existing online resources. In this work, we propose a novel data engine designed to improve the removal of complex moir'e patterns by generating training supervision. Specifically, we initially collect real-world images containing complex moir'e patterns and localize the corresponding screen regions. Multiple image-conditioned generative foundation models are subsequently deployed to produce candidate references. To establish reliable supervision, these candidates are subjected to patch-level quality control to filter and select the optimal results. Based on this systematic paradigm, we construct the WildMoir'e dataset, which contains 6.8K moir'e-GT training pairs. For evaluation, we additionally build an independent test set comprising ~250 pairs with captured clean ground truth. Extensive experiments on ESDNet, SDXL, and Qwen-Image-Edit demonstrate that the proposed generative supervision consistently improves the performance of complex moir'e removal.