Improving the quality of light‐field data extracted from a hologram using deep learning

• Published in: ETRI journal 2024, 46(2), , pp.165-174
• Main Authors: Park, Dae‐youl, Park, Joongki
• Format: Article
• Language: English
• Subjects: deep learning; generative adversarial network; holography; light field; speckle noise; 전자/정보통신공학
• ISSN: 1225-6463; 2233-7326
• DOI: 10.4218/etrij.2022-0441

We propose a method to suppress the speckle noise and blur effects of the light field extracted from a hologram using a deep‐learning technique. The light field can be extracted by bandpass filtering in the hologram's frequency domain. The extracted light field has reduced spatial resolution owing to the limited passband size of the bandpass filter and the blurring that occurs when the object is far from the hologram plane and also contains speckle noise caused by the random phase distribution of the three‐dimensional object surface. These limitations degrade the reconstruction quality of the hologram resynthesized using the extracted light field. In the proposed method, a deep‐learning model based on a generative adversarial network is designed to suppress speckle noise and blurring, resulting in improved quality of the light field extracted from the hologram. The model is trained using pairs of original two‐dimensional images and their corresponding light‐field data extracted from the complex field generated by the images. Validation of the proposed method is performed using light‐field data extracted from holograms of objects with single and multiple depths and mesh‐based computer‐generated holograms.