使用压缩自编码器进行变量贝叶斯图像恢复
概括
本研究介绍了用于计算成像中的反向问题的变量贝叶斯隐藏估计 (VBLE). VBLE使用压缩自编码器和变异推理,比目前的方法更快地定量不确定性.
科学领域:
- 计算成像技术的成像
- 机器学习 机器学习
- 贝叶斯的推理是贝叶斯的推理.
背景情况:
- 规则化对于解决计算成像中的反向问题至关重要.
- 神经网络提供了强大的数据驱动调节器,使用插件和播放 (PnP) 方法,使用从denoisers中隐式调节.
- 贝叶斯方法在生成模型中使用通过后期最大值 (MAP) 估计的显式规范化,但这些模型数据密集且复杂.
研究的目的:
- 为规范化提出压缩自编码器作为复杂的深度生成模型的更有效的替代方案.
- 引入变量贝叶斯潜估算 (VBLE) 算法,以使用变量推理进行高效的潜估算.
- 为了实现反向问题的快速和准确的不确定性量化.
主要方法:
- 使用压缩自编码器,一种带有灵活潜伏前置的变化自编码器,用于网络训练.
- 开发了变量贝叶斯潜估算 (VBLE) 算法,使用变量推理进行潜估算.
- 实现了一个简单而高效的变化后部参数化,以促进后部快速采样.
主要成果:
- 在BSD和FFHQ图像数据集上,VBLE的性能与最先进的PnP方法相美.
- 与现有的后端采样技术相比,VBLE显示不确定性量化速度明显更快.
- 压缩自动编码器比传统的深度生成模型更小,更容易训练.
结论:
- 在反向问题中,VBLE提供了一种计算高效和有效的规范化方法.
- 该方法为现有的PNP和贝叶斯技术提供了可行的替代方案,特别是当数据有限或计算资源受到限制时.
- 在成像应用中的不确定性量化中,VBLE成功地平衡了性能与速度.
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