Yangeng Zhao, Bing Dong, Ming Liu, Zhiqiang Zhou, Jing Zhou. Deep Learning Based Computational Ghost Imaging Alleviating the Effects of Atmospheric Turbulence[J]. Acta Optica Sinica, 2021, 41(11): 1111001
- Acta Optica Sinica
- Vol. 41, Issue 11, 1111001 (2021)
Fig. 1. Flow chart of image classification-restoration method used for computational ghost imaging
Fig. 2. Classification network structure
Fig. 3. Network structure of generator
Fig. 4. Network structure of discriminator
Fig. 5. Schematic diagram of computational ghost imaging through atmospheric turbulence
Fig. 6. Influence of atmospheric turbulence of different intensities on computational ghost imaging. (a) Initial speckle field and object; (b) speckle field and reconstructed images without turbulence; (c)--(g) speckle field and reconstructed images with different turbulence strengths
Fig. 7. Classified results of blurred images by classification network
Fig. 8. Simulation results of restoration network. (a) Blurred images; (b) restored images using classification-restoration network; (c) restored images only using restoration network
Fig. 9. Image evaluation index of blurred images and restored images under each category. (a) PSNR mean value; (b) SSIM mean value
Fig. 10. Restoration results for other types of images
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