使用随机的尼斯特罗姆预条件器来加速变量图像重建.
概括
这项研究引入了加速图像重建的新型先决条件. 它使用随机的尼斯特罗姆近似和GPU来有效地解决复杂的反向问题,而不需要明确的前向模型.
科学领域:
- 计算机成像成像技术
- 应用数学 应用数学 应用数学
- 科学计算是科学计算.
背景情况:
- 基于模型的代重建对于反向问题至关重要,但面临着大规模,不平滑和不凸最小化的挑战.
- 需要有效的代溶解器,预条件化方法可以加速融合.
- 图像重建中的前进模型通常是运算符,缺乏明确的矩阵,使预条件设计复杂化.
研究的目的:
- 开发用于加速图像重建的计算成本低廉且有效的先决条件.
- 为了应对前模型不可用的显式矩阵的挑战.
- 通过使用现代硬件实现即时计算和预先条件的应用.
主要方法:
- 适应随机的尼斯特罗姆近似来计算先决条件.
- 利用GPU计算平台进行即时预先调节计算.
- 开发非光滑调节器的高效应用方法 (波形,总变量,赫西安沙顿规范).
主要成果:
- 证明了图像重建趋同的加速.
- 在不需要明确的前模型矩阵的情况下计算的有效预先条件.
- 在图像消除模糊,超分辨率与冲动噪声和2D计算机断层扫描方面成功应用.
结论:
- 提出的基于尼斯特罗姆的随机预条件器对于加速图像重建是高效和有效的.
- 机动GPU计算使该方法在现实应用中变得实用.
- 这种方法成功地处理了各种不平滑的调节器和重建任务.
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