Fig. 1. Schematic diagram of VGG16-ECNN,and convolution process. (a) Schematic diagram of VGG16-ECNN; (b) convolution process

Fig. 2. Schematic diagrams of VGG16-DONN structure. (a) Simplified diagram of VGG16-ECNN; (b) 4f system [fx,y is input complex amplitude function, Fu,v is Fourier transform of fx,y, and fξ,η is reconstructed signal restored by second convex lens]

Fig. 3. Weights of convolution kernels

Fig. 4. Dataset examples. (a) CelebA dataset; (b) cat and dog dataset

Fig. 5. Training flow chart

Fig. 6. Original image and output images of two network structures through partial convolution kernel in the first convolution layer.(a) Face image; (b) VGG16-ECNN; (c) VGG16-DONN

Fig. 7. Output of face image convolved with all convolution kernels in the first convolution layer

Fig. 8. Output graph of each convolution block after inputting face image to VGG16-DONN structure. (a) Conv1; (b) Conv2; (c) Conv3; (d) Conv4; (e) Conv5

Fig. 9. Original image and output graphs of two network structures through partial convolution kernel in the first convolution layer.(a) Dog image; (b) VGG16-ECNN; (c) VGG16-DONN

Fig. 10. VGG16-DONN for CDP

Fig. 11. OWM algorithm flow chart

Fig. 12. Training results of VGG16-DONN and VGG16-ECNN. (a) Training accuracy and validation accuracy of VGG16-DONN and VGG16-ECNN; (b) training loss and validation loss of VGG16-DONN and VGG16-ECNN

Fig. 13. Training/validation accuracy and time obtained by varying different parameters. (a) Weights of different layers trained by VGG16-ECNN structure; (b) weights of different layers trained by VGG16-DONN structure; (c) VGG16-DONN structure changes optimizer; (d) VGG16-DONN structure changes learning rate