DF-ACBlurGAN: Structure-Aware Conditional Generation of Internally Repeated Patterns for Biomaterial Microtopography Design

TL;DR

This paper introduces DF-ACBlurGAN, a structure-aware generative model that explicitly reasons about long-range repetition to improve the design of biomaterial microtopographies with controlled periodic structures.

Contribution

The work presents a novel GAN architecture that incorporates frequency-domain analysis and scale-adaptive blurring to enhance global structural consistency in generated patterns.

Findings

Improved repetition consistency over traditional methods

Enhanced control over structural variation in generated designs

Effective synthesis of biomaterial microtopographies aligned with biological responses

Abstract

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.