Fig. 1. Schematic diagram of the ghost imaging edge detection scheme using Unet convolutional neural network

Fig. 2. Unet ghost imaging edge detection network (conv 3×3: convolutional kernel of size 3×3; relu is the activation function; BN: batch normalization layer; maxpool 2×2: maximum pooling layer of size 2×2; maxunpool 2×2: maximum inverse pooling layer of size 2×2; skip connection: jump connection to sum the encoded information with decoded information; sigmoid: activation function, maps input values to 0−1 probabilities)

Fig. 3. Sample images from the dataset

Fig. 4. Training loss variation process and learning rate variation curve of Unet ghost imaging edge extraction network

Fig. 5. Output edge images of Unet ghost imaging edge detection network for the test set and the edge images processed by the non-maximal value suppression algorithm

Fig. 6. Output images of Unet ghost imaging edge detection network corresponding to multiple letters or numbers

Fig. 7. Comparison of the edge results output by the Unet ghost imaging edge detection network for the letter K and the number 7 with those output by the scatter-shift ghost imaging method

Fig. 8. SNR and SSIM metrics of the edge results output by the Unet ghost imaging edge detection network compared to those output by the scatter-shift ghost imaging method as the number of samples increases for the letter K and the number 7