# Progress of the Computer-Generated Holography Based on Deep Learning
## 4 The Learning-Based CGH
### GAN-Based Deep CGH  
In addition to the ResNet-based networks and U-Net-based networks, it is also effective to use a GAN network to cut down the huge calculation time of the iterative CGH methods. GAN networks have gained a lot of attention in the computer vision community due to their data generation capability without explicitly modeling the probability density function [52].  
In 2020, [PERSON] et al. proposed a new method using GAN-based DNN for holography. The framework consisted of two phases. In phase one, the Fresnel-based method was used to make the dataset. In phase two, the raw input image and holographic label image data from phase one acquired images were trained to improve the accuracy and efficiency. The training dataset was composed of the handwritten digit dataset and its corresponding holograms. The label holograms, as the ground truth, were generated by the Fresnel method. To generate a single hologram, an Intel CoreTM i7-7820X CPU @ 3.60 GHz and a GeForce GTX 1080 (NVIDIA) were used. The training time for GAN-Holo was 20 h. The computational time based on GAN-Holo and the Fresnel method was 5 ms and 94 ms, respectively. The root mean square error (RMSE) between the reconstructed images and target intensity patterns obtained by the proposed method was below 0.1, which performed fairly well [27].  
In 2021, to improve the reconstruction quality, [PERSON] et al. proposed a GAN-based DNN for fringe pattern generation. The fake data were inferred using the DNN-based fringe pattern generator and compared with the real data by using the discriminator. Then, the parameters of the discriminator and generator were updated simultaneously. By repeating this process, the real and fake became indistinguishably similar. NVIDIA Tesla V100 (32 Gb) and Intel Xeon Gold [EMAIL_ADDRESS] GHz were used as the GPU and CPU. Two datasets of 16 and 32 spaces were constructed by using object points that existed within a certain space for training. It took about 520 h and 1944 h for the 16 and 32 spaces to learn the real and imaginary parts, respectively. After completing the training, it took 360 us to generate a fringe pattern for each one. The average PSNR in the 16 space was 44.56 dB, and the average PSNR in the 32 space was 35.11 dB [25]. The training process was time-consuming, but the CGH calculation after the training was very fast with a relatively high PSNR.  
Aiming to generate a hologram with spatially varying aberration compensation, [PERSON] et al. proposed a new method using a combination of an FFT-based convolution and a neural network [26]. The optical aberrations of the holographic display were denoted. The training dataset was composed of pairs of various images and corresponding aberration compensation maps. The compensation maps were synthesized by the pointwise integration method. Then, a propagator was designed to reconstruct an image close to the target image. The NVIDIA Tesla P40 GPU with a 24 GB memory was used for the generation and the 800 training images of the DIV2K dataset were exploited as the target images. The parameters of the generator were optimized by using the dataset and the results of the propagator. The main network in the propagator was U-Net and the general idea of the proposed propagator was based on GAN. The optical experiments verified the performance of the technique for aberration compensation.