DCPNet:一个双通道并行深度神经网络,用于高质量的计算机生成全息.
Optics express
|November 29, 2023
概括
一个新的双通道并行神经网络 (DCPNet) 为高质量的全息显示器生成单相全息图 (POH). 这种方法改善了细节的保存,并减少了重建图像中的噪声.
科学领域:
- 光学是什么?光学是什么?光学是什么?
- 计算机科学 计算机科学
- 全息影像的使用方法.
背景情况:
- 基于学习的计算机生成全息图 (CGH) 显示出对实时,高质量的全息显示的希望.
- 现有的算法经常将复杂值的波场处理为双通道图像,不完全利用复杂的振幅特征.
研究的目的:
- 提出一种新的双通道并行神经网络 (DCPNet),用于高效的单相全息图 (POH) 生成.
- 通过更好地考虑复杂的幅度计算特征来解决当前方法的局限性.
主要方法:
- 开发了一种基于双相振幅编码的双通道并行神经网络 (DCPNet).
- 将复杂值的波场编码为两个实值的相位元素,而不是一个双通道图像.
- 通过采样两个学习的子POH与互补的2D二进制格子来合成POH.
主要成果:
- 在模拟中,DCPNet在36毫秒 (ms) 中实现了高保真2k POH生成.
- 光学实验表明,在重建的图像中,更细微的细节得到了优异的保存.
- 该方法有效地抑制了斑点噪声,并改善了图像的均性.
结论:
- 拟议的DCPNet提供了一种有效和高效的方法来生成仅相位的全息图.
- 这种方法显著提高了重建的全息图像的质量.
- DCPNet有可能推进实时,高质量的全息显示技术.
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