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Efficient Computer-Generated Holography Based on Mixed Linear Convolutional Neural Networks
| Content Provider | MDPI |
|---|---|
| Author | Xu, Xianfeng Wang, Xinwei Luo, Weilong Wang, Hao Sun, Yuting |
| Copyright Year | 2022 |
| Description | Imaging based on computer-generated holography using traditional methods has the problems of poor quality and long calculation cycles. However, recently, the development of deep learning has provided new ideas for this problem. Here, an efficient computer-generated holography (ECGH) method is proposed for computational holographic imaging. This method can be used for computational holographic imaging based on mixed linear convolutional neural networks (MLCNN). By introducing fully connected layers in the network, the suggested design is more powerful and efficient at information mining and information exchange. Using the ECGH, the pure phase image required can be obtained after calculating the custom light field. Compared with traditional computed holography based on deep learning, the method used here can reduce the number of network parameters needed for network training by about two-thirds while obtaining a high-quality image in the reconstruction, and the network structure has the potential to solve various image-reconstruction problems. |
| Starting Page | 4177 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app12094177 |
| Journal | Applied Sciences |
| Issue Number | 9 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-04-21 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Digital Holography Computer-generated Holography (cgh) Deep Learning Image Reconstruction Neural Network |
| Content Type | Text |
| Resource Type | Article |
