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针对无光学成像和分类的客观受约束周期一致的深度学习

Soren Nelson¹, Rajesh Menon¹

¹Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, USA.

|June 28, 2023

概括

这项研究引入了一种新的深度学习模型,用于无光学成像. 它学习前向和反向模型,使更薄的摄像头和从裸体图像传感器的各种图像重建成为可能.

科学领域:

计算机成像成像技术
深度学习是一种深度学习.
计算机视觉计算机视觉计算机视觉

背景情况:

数据驱动的深度学习方法在计算成像方面表现出色,但与来自裸体图像传感器的结构多样化的图像作斗争.
现有的方法通常仅依赖于数据,缺乏强大的重建约束.

研究的目的:

开发一个自我一致的监督模型,以改善无光学图像重建.
通过学习前向和反向成像模型来限制深度学习预测.
为了使超薄摄像机的发展成为可能.

主要方法:

提出了一种自我一致的监督深度学习模型.
与传统的重建损失相结合的整合周期一致性.
训练网络以建模一个理想的对比成像系统.

主要成果:

从原始,无光学传感器数据成功地重建了结构多样化的目标图像.
证明了不连贯光学无成像的循环一致性和重建损失的必要性.
通过显著降低相机配置来实现成像能力.

结论:

拟议的模型有效地解决了纯数据驱动方法在无光学成像中的局限性.
学习前模型与反向模型一起,为准确的重建提供了关键的约束.
这种方法为新的超薄相机设计铺平了道路.

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