用生成模型设计生物材料微结构，让重复图案更精确可控
DF-ACBlurGAN: Structure-Aware Conditional Generation of Internally Repeated Patterns for Biomaterial Microtopography Design
- 通过频域分析和自适应模糊，让模型理解长距离重复规律
- 生成图案的周期性一致性提升，结构变化可精准控制
- 适合需要精确重复结构的生物材料设计，如组织工程表面
学习生成具有内部重复和周期性结构的图像，是机器学习与计算机视觉中的基础挑战。现有模型通常优化局部纹理统计与语义真实性，而非全局结构一致性。这一局限在需严格控制重复尺度、间距与边界连贯性的应用中尤为突出，例如微拓扑生物材料表面。本文以生物材料设计为应用场景，研究弱监督和类别不平衡下的重复图案条件生成。提出DF-ACBlurGAN，一种结构感知的条件生成对抗网络，在训练中显式建模长程重复性。方法结合频域重复尺度估计、尺度自适应高斯模糊与单元胞重建，平衡局部细节清晰度与全局周期稳定性。基于实验获得的生物响应标签进行条件生成，合成的设计与目标功能结果对齐。在多个生物材料数据集上的评估表明，相比传统生成方法，本模型显著提升了重复一致性与可控结构变异能力。
原文摘要 · Abstract (English)
Learning to generate images with internally repeated and periodic structures poses a fundamental challenge for machine learning and computer vision models, which are typically optimised for local texture statistics and semantic realism rather than global structural consistency. This limitation is particularly pronounced in applications requiring strict control over repetition scale, spacing, and boundary coherence, such as microtopographical biomaterial surfaces. In this work, biomaterial design serves as a use case to study conditional generation of repeated patterns under weak supervision and class imbalance. We propose DF-ACBlurGAN, a structure-aware conditional generative adversarial network that explicitly reasons about long-range repetition during training. The approach integrates frequency-domain repetition scale estimation, scale-adaptive Gaussian blurring, and unit-cell reconstruction to balance sharp local features with stable global periodicity. Conditioning on experimentally derived biological response labels, the model synthesises designs aligned with target functional outcomes. Evaluation across multiple biomaterial datasets demonstrates improved repetition consistency and controllable structural variation compared to conventional generative approaches.
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