在同质扩散设置中连接图像inpainting与denoising
Daniel Gaa1, Vassillen Chizhov1, Pascal Peter1
1Mathematical Image Analysis Group, Faculty of Mathematics and Computer Science, Saarland University, Campus E1.7, 66041 Saarbrücken, Germany.
Advances in continuous and discrete models
|March 31, 2025
概括
这项研究将图像染和inpainting使用染通过inpainting框架联系起来. 它表明,在扩散模型中,数据适应性可以和操作员适应性一样强大.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 数学建模的数学建模
背景情况:
- 在图像无色化和inpainting中的本地方法具有概念上的相似性.
- 这两个图像处理任务之间的关系仍然未被充分探索.
研究的目的:
- 为了建立一个理论上的图像denoising和inpainting之间的联系.
- 在同质扩散环境中研究通过涂漆 (DbI) 进行脱雾的框架.
主要方法:
- 研究了一种Denoising by inpainting (DbI) 框架,该框架的平均值是多个inpainting结果.
- 在1D中,DbI和均质扩散过之间的衍生等价性.
- 经验扩展了发现到二维和非均的扩散场景.
主要成果:
- 对于1D DbI和均质扩散,建立了密度和扩散时间之间的明确关系.
- 实验结果证实了理论发现.
- 证明了对非均数据和扩散性进行潜在的概括.
结论:
- 这项研究通过统一的传播框架来弥合消毒和染色.
- 强调了图像处理模型中数据适应性的重要性.
- 表明数据适应性可以在性能上与操作员适应性竞争.
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