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Computational ghost imaging with compressed sensing based on a convolutional neural network
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DOI：10.3788/COL202119.101101.png)
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摘要
En 中文
Computational ghost imaging (CGI) has recently been intensively studied as an indirect imaging technique. However, the image quality of CGI cannot meet the requirements of practical applications. Here, we propose a novel CGI scheme to significantly improve the imaging quality. In our scenario, the conventional CGI data processing algorithm is optimized to a new compressed sensing (CS) algorithm based on a convolutional neural network (CNN). CS is used to process the data collected by a conventional CGI device. Then, the processed data are trained by a CNN to reconstruct the image. The experimental results show that our scheme can produce higher quality images with the same sampling than conventional CGI. Moreover, detailed comparisons between the images reconstructed using the deep learning approach and with conventional CS show that our method outperforms the conventional approach and achieves a ghost image with higher image quality.
Keyword:
computational ghost imaging
compressed sensing
convolutional neural network
