Abstract We propose a deep learning denoising computational ghost imaging (CGI) method to obtain a clear object image with a sub-Nyquist sampling ratio. We develop an end-to-end deep neural network (DDANet) for CGI image reconstruction. DDANet uses a one-dimensional (1-D) bucket signals (BSs) and multiple tunable noise-level maps as input, and outputs a clear image. We train DDANet with simulated BSs and ground-truth pairs, and then retrieve the object image directly from an experimental obtained 1-D BSs. The effectiveness of the proposed method is experimentally investigated. The proposed method has practical applications in image denoising and enhancement of the CGI and single-pixel computational imaging.

中文翻译：

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深度学习去噪计算鬼成像

摘要 我们提出了一种深度学习去噪计算鬼成像 (CGI) 方法，以获得具有亚奈奎斯特采样率的清晰物体图像。我们开发了用于 CGI 图像重建的端到端深度神经网络 (DDANet)。DDANet 使用一维 (1-D) 桶信号 (BS) 和多个可调噪声水平图作为输入，并输出清晰的图像。我们用模拟 BS 和地面实况对训练 DDANet，然后直接从实验获得的一维 BS 中检索对象图像。实验研究了所提出方法的有效性。所提出的方法在CGI和单像素计算成像的图像去噪和增强方面具有实际应用。