基于二元分区的培训方案,用于电视/TGV的剥离.
Elisa Davoli1, Rita Ferreira2, Irene Fonseca3
1Institute of Analysis and Scientific Computing, TU Wien, Wiedner Hauptstrasse 8-10, 1040 Vienna, Austria.
Journal of mathematical imaging and vision
|November 14, 2024
概括
本研究引入了一种使用多层次方法自动图像无声化参数调整的新方法. 它优化了二元格的空间依赖参数,比恒定参数提高了性能.
科学领域:
- 图像处理和计算机视觉
- 数学优化的数学优化
- 应用数学 应用数学 应用数学
背景情况:
- 总变化 (TV) 和总通用变化 (TGV) 是有效的图像消噪方法,但需要仔细调整参数.
- 图像消噪的变化模型通常严重依赖于参数选择,影响性能.
- 需要自动参数选择方法,如多层次方法,以克服这种局限性.
研究的目的:
- 开发和分析一个多层次的方法,用于自动图像 denoising 参数选择.
- 为了研究空间依赖的参数,这些参数在二次格子上是零碎的常数,格子结构是优化的一部分.
- 证明这些空间依赖参数的最小化器和最佳分区的存在.
主要方法:
- 在温和数据假设下,证明固定不连续参数的最小化器的存在.
- 确定这些假设与参数标准框限制的等价性.
- 开发一个最佳分区的分区方案,并与标量参数的双级优化集成.
主要成果:
- 对于空间依赖参数的最小化器和有限的最佳分区的存在已被证明.
- 与使用常量优化参数的方法相比,拟议的方法在测试图像上表现出更好的无色化性能.
- 这个数值方案有效地优化了二元格的零位常数,空间依赖的参数.
结论:
- 在电视和TGV图像消噪中,对空间依赖参数的自动调整是可行的和有效的.
- 开发的双层网格的多层次方法为图像无线化中的参数选择提供了强大的解决方案.
- 这项工作通过提供更适应性和高性能参数优化策略来推进图像处理中的变量方法.
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