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Updated: Jan 10, 2026

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Visualizing Visual Adaptation
Published on: April 24, 2017
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通过将扩散与规范化数据集成,为彩色图像扩散提供混合变异模型的局部自适应规范化
Muzaffar Bashir Arain1, Khuda Bux Amur2, Seham Sh Tantawy3
1Department of Mathematics & Statistics, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, 67480, Sindh, Pakistan. muzaffararain@quest.edu.pk.
Scientific reports
|November 21, 2025
概括
这项研究引入了用于色彩图像无色化,增强边缘保护和降噪的自适应规范化. 新的混合变异模型比传统方法提供了更高的性能,特别是在高噪音条件下.
科学领域:
- 图像处理 图像处理
- 计算机视觉 计算机视觉
- 应用数学 应用数学 应用数学
背景情况:
- 图像无色化对于视觉数据质量至关重要.
- 传统的方法在消除噪音的同时保护细节方面扎.
- 变化模型为图像恢复提供了一个强大的框架.
研究的目的:
- 开发一种适应性规范化控制,用于用于色彩图像消噪的混合变化模型.
- 为了增强降噪和边缘保护能力.
- 为了提高在高噪声场景中的性能,并减轻人工物.
主要方法:
- 一种混合变化模型,结合了总变化 (TV) 和调节器.
- 一个适应性控制参数用于局部选择扩散操作员 (平滑的正方形,边缘保护的非正方形).
- 规范化数据忠实性和补充数据规范化,以改善平衡和工件减轻.
- 使用有限差异对依赖时间的部分微分方程进行分离.
主要成果:
- 与传统技术相比,拟议的方法在边缘保护和降噪方面表现优越.
- 使用PSNR,SSIM,RMSE和CT进行的定量评估显示了显著的改善.
- 适应性方法有效地抑制了噪音,同时保留了尖的结构细节.
结论:
- 混合变异模型中的自适应规范化控制对彩色图像无色化有效.
- 该方法在降噪和保存细节之间提供了良好的平衡.
- 它的性能优于最先进的变化无声化模型,特别是在高噪音水平下.
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