Hao Zhang, Deyang Duan. Computational ghost imaging with compressed sensing based on a convolutional neural network[J]. Chinese Optics Letters, 2021, 19(10): 101101
- Chinese Optics Letters
- Vol. 19, Issue 10, 101101 (2021)
Fig. 1. Setup of the CGI system with CS-CNN. SLM, spatial light modulator; BD, bucket detector.
Fig. 2. Network structure of the proposed CS-CNN.
Fig. 3. Ghost images reconstructed by CGI with CS-CNN. (a1) Classical image. The numbers of frames in the reconstructed ghost images are (a2) 30, (a3) 50, (a4) 70, and (a5) 90. (b) PSNR and SSIM curves of the reconstructed images with different frame numbers.
Fig. 4. Detailed comparison between the ghost images reconstructed using the conventional CS algorithm, DL algorithm, and CS-CNN algorithm. The number of frames is (a) 30, (b) 50, (c) 70, and (d) 90.
Fig. 5. PSNR and SSIM curves of reconstructed images of CS, DL, and CS-CNN with different frame numbers.
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