发表机构
International University, VNU-HCM（越南国立大学胡志明市国际大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出内部引导的边界修复方法，通过内部引导传播和内向距离混合修复多区域风格化中的边界伪影，无需重训练即可集成，并在边界一致性、梯度稳定性和内部保留上优于现有方法。
AI 中文摘要
基于区域的神经风格迁移通过允许对语义图像区域进行独立风格化，实现了精细的艺术控制。然而，将这些区域合成在一起常常导致边界伪影，降低视觉质量。我们提出了内部引导的边界修复（IGBR），一种轻量级且模型无关的方法，用于改善多区域风格化中的边界处理。IGBR使用内部引导的传播修复边界像素，并应用仅限于对象-背景边界的向内、基于距离的混合，防止对象间风格泄漏。该方法源于区域约束的公式化，具有闭式解，无需重新训练即可无缝集成到现有的风格化流程中。为了评估IGBR的效率，我们引入了定量指标，用于测量边界一致性、梯度伪影、对象间泄漏和内部保留，无需注释的风格化图像。我们的实验和评估表明，所提出的IGBR始终产生合理的边界，在边界一致性、梯度稳定性和内部保留方面优于先前的混合技术。代码可在以下网址获取：https://this https URL。
英文摘要
Region-based neural style transfer enables fine-grained artistic control by allowing independent stylization of semantic image regions. However, compositing these regions often leads to boundary artifacts, degrading visual quality. We propose Interior-Guided Boundary Repair (IGBR), a lightweight and model-agnostic method that improves boundary handling in multi-region stylization. IGBR repairs boundary pixels using interior-guided propagation and applies inward, distance-based blending restricted to object-background boundaries, preventing inter-object style leakage. The method is derived from a region-wise constrained formulation with a closed-form solution and can be seamlessly integrated into existing stylization pipelines without retraining. To evaluate efficiency of our IGBR, we introduce quantitative metrics that measure boundary consistency, gradient artifacts, inter-object leakage, and interior preservation without requiring annotated stylized images. Our experiments and evaluations demonstrate that the proposed IGBR consistently produces plausible boundaries, outperforming prior blending techniques in boundary consistency, gradient stability, and interior preservation. The code is available at https://github.com/Son-SDT/IGBR.
Comments10 pages, 7 figures
