通过模型增强网络进行单拍图像恢复,在光学稀疏光圈系统中进行无配对监控
Optics letters
|September 14, 2023
概括
我们开发了一种新的方法,使用模型增强的网络和未配对的数据来从光学稀疏光圈 (OSA) 系统中恢复单拍图像,提高清晰度和减少噪音.
科学领域:
- 光学和光子学 在光学和光子学.
- 计算成像技术的成像
- 机器学习 机器学习
背景情况:
- 光学稀疏光圈 (OSA) 系统由于其设计而遭受图像模糊和噪声.
- 现有的恢复方法通常需要多个图像或特定的系统知识.
研究的目的:
- 为OSA系统开发一种有效的单拍图像恢复方法.
- 为了应对OSA图像中模糊,噪音和低对比度的挑战.
主要方法:
- 一个级联的神经网络以不配对的单拍退化图像和任意选择的清晰图像进行代训练.
- 整合了一个计算图像退化模型以增强.
- 网络执行消音和恢复.
主要成果:
- 在模拟和实验OSA图像中成功改善了对比度,恢复了模糊,并抑制了噪声.
- 与具有较少先前要求的其他算法相比,实现了优越的恢复性能.
- 对于任何OSA系统来说,已证明有效的单拍图像恢复.
结论:
- 拟议的模型增强网络为OSA系统中一次性图像恢复提供了强大的解决方案.
- 该方法在选择未配对清晰图像方面的灵活性以及其不严格的核心要求提高了其实际适用性.
- 这种方法显著提高了OSA成像系统的功能.
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