Opt Lett. 2021 Apr 15;46(8):1840-1843. doi: 10.1364/OL.418628.
A novel, to the best of our knowledge, color computational ghost imaging scheme is presented for the reconstruction of a color object image, which greatly simplifies the experimental setup and shortens the acquisition time. Compared to conventional schemes, it only adopts one digital light projector to project color speckles and one single-pixel detector to receive the light intensity, instead of utilizing three monochromatic paths separately and synthesizing the three branch results. Severe noise and color distortion, which are common in ghost imaging, can be removed by the utilization of a generative adversarial network, because it has advantages in restoring the image's texture details and generating the image's match to a human's subjective feelings over other generative models in deep learning. The final results can perform consistently better visual quality with more realistic and natural textures, even at the low sampling rate of 0.05.
提出了一种新颖的、据我们所知的彩色计算鬼成像方案，用于重建彩色物体图像，这大大简化了实验设置并缩短了采集时间。与传统方案相比，它仅采用一个数字光投影仪来投影彩色散斑，和一个单像素探测器来接收光强，而不是利用三个单色路径分别并合成三个分支结果。通过利用生成对抗网络，可以去除鬼成像中常见的严重噪声和颜色失真，因为它在恢复图像的纹理细节和生成图像与人的主观感受的匹配方面具有优势，而优于深度学习中的其他生成模型。即使在 0.05 的低采样率下，最终结果也能以更逼真和自然的纹理呈现出一致更好的视觉质量。
