关于深度学习用于阶段恢复的使用
Kaiqiang Wang1,2,3, Li Song4, Chutian Wang4
1Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China. kqwang.optics@gmail.com.
Light, science & applications
|December 31, 2023
概括
深度学习 (DL) 通过改进计算成像来增强阶段恢复 (PR). 这篇评论探讨了DL的研究.
科学领域:
- 计算机成像成像技术
- 光学是什么?光学是什么?光学是什么?
- 机器学习是机器学习.
背景情况:
- 阶段恢复 (PR) 对于重建对象属性和纠正成像误差至关重要.
- 传统的公关方法存在,但在效率和范围上可能受到限制.
- 深度学习 (DL) 为成像应用提供先进的计算工具.
研究的目的:
- 审查DL在阶段恢复中的应用.
- 将DL在PR中的作用分类为前处理,在处理和后处理阶段.
- 讨论DL对相位图像处理和未来前景的影响.
主要方法:
- 审查传统的阶段恢复技术.
- 对应用到阶段恢复的深度学习方法的分析.
- 在PR管道中的DL应用程序的分类.
主要成果:
- DL显著提高了各种公关问题的效率和解决方案.
- DL集成跨越了PR的预处理,在处理和后处理阶段.
- DL增强了相位图像处理能力.
结论:
- DL是一种强大的工具,用于推进计算成像中的相位恢复.
- 使用DL的进一步研究可以提高PR的可靠性和效率.
- 提供了一个不断更新的资源,以进一步学习关于DL的PR.
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