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Parametric kernels for artifact mitigation in patch-based image aggregation using generative models
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DOI：10.1016/j.cviu.2025.104457.png)
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Abstract
En 中文
• Novel parametric kernel for mitigating artifacts in patch-based image aggregation. • Our kernel outperforms existing methods across multiple medical imaging modalities. • Versatile and adaptable formulation, independent of the generative model. • Quantitative evaluation demonstrates improved visual quality and reduced artifacts. • Promising applicability to various large field-of-view imaging tasks.
Keywords:
Checkerboard artifacts
Generative models
Gigapixel
Medical imaging
Patch aggregation
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