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从缺失的作品变成了杰作:用上下文适应的扩散来完成图像

Pourya Shamsolmoali, Masoumeh Zareapoor, Huiyu Zhou

IEEE transactions on pattern analysis and machine intelligence

|April 4, 2025

概括

通过使用上下文适应差异 (CAD) 模型对准已知和未知图像区域,ConFill提高了图像的完整性. 这种新的框架确保了无集成和在生成的内容中增强了细节,优于现有的方法.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
机器学习机器学习

背景情况:

图像完成很困难,扩散模型努力将生成的内容与现有图像部分连贯地融合在一起.
现有的方法缺乏明确的空间和语义对齐,导致不一致性和糟糕的整合.

研究的目的:

介绍ConFill,一个用于高保真图像完成的新框架.
解决基于扩散的图像完成中的连贯性和整合性挑战.

主要方法:

开发了一个上下文适应差异 (CAD) 模型,以对齐已知和未知的图像区域的中间分布.
实施了动态采样机制,以适应性地完善复杂地区的采样率.

主要成果:

在每个扩散步骤中,ConFill 逐渐减少生成和原始图像内容之间的差异.
动态采样机制提高了恢复区域的细节性和整合性.
广泛的实验表明ConFill超越了当前最先进的图像完成方法.

结论:

ConFill通过实现与上下文一致且无集成的结果,为图像完成建立了新的基准.
拟议的CAD模型和动态采样机制有效地解决了生成模型中的连贯性问题.

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