Ren Wenhan, Nie Xiaoyu, Peng Tao, Scully Marlan O
Opt Express. 2022 Dec 19;30(26):47921-47932. doi: 10.1364/OE.478695.
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.
人工智能最近在计算成像中得到了广泛应用。深度神经网络（DNN）提高了检索图像的信噪比，否则由于低采样率或噪声环境，图像质量会受到损害。这项工作提出了一种基于具有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转换能力。实验性单像素探测器的信号将以端到端的方式“转换”为二维图像。在低至2%的采样率下，可以检索到无背景噪声的高质量图像。照明图案可以是用于亚奈奎斯特成像的精心设计的散斑图案，也可以是随机散斑图案。此外，我们的方法对噪声干扰具有鲁棒性。这种转换机制为DNN辅助的鬼成像开辟了一个新方向，可用于各种计算成像场景。
