论文标题
幽灵翻译
Ghost translation
论文作者
论文摘要
人工智能最近被广泛用于计算成像中。深神经网络（DNN）改善了检索到的图像的信噪比，由于采样比或嘈杂的环境，其质量否则会损坏其质量。这项工作提出了一种基于变压器网络的序列转导机制的新计算成像方案。模拟数据库有助于网络实现信号翻译能力。实验单像素检测器的信号将以端到端的方式“翻译”成2D图像。没有背景噪声的高质量图像可以以低至2％的抽样比率检索。照明模式可以是精心设计的斑点模式，用于子nyquist成像或随机斑点模式。此外，我们的方法对噪声干扰是可靠的。这种翻译机制为DNN辅助幽灵成像打开了一个新的方向，可用于各种计算成像方案。
Artificial intelligence has recently been widely used in computational imaging. The deep neural network (DNN) improves the signal-to-noise ratio of the retrieved images, whose quality is otherwise corrupted due to the low sampling ratio or noisy environments. This work proposes a new computational imaging scheme based on the sequence transduction mechanism with the transformer network. The simulation database assists the network in achieving signal translation ability. The experimental single-pixel detector's signal will be `translated' into a 2D image in an end-to-end manner. High-quality images with no background noise can be retrieved at a sampling ratio as low as 2%. The illumination patterns can be either well-designed speckle patterns for sub-Nyquist imaging or random speckle patterns. Moreover, our method is robust to noise interference. This translation mechanism opens a new direction for DNN-assisted ghost imaging and can be used in various computational imaging scenarios.
