Image-to-image translation for improved digital holographic reconstruction based on a generative adversarial network learning framework

Based on the generative-discriminative model, a digital holographic reconstruction generative adversarial network is established (DHR-GAN), in which the objective function is optimized on the original model to evaluate the perceptual holographic image. Performing the goals of different optical characteristics, the experimental acquisition system of digital holographic images is designed and constructed for preparing the digital holographic reconstructed image dataset (DHR-dataset), which achieves supervised training and testing of DHR-GAN. From an extensive qualitative and quantitative comparative discussion, the reconstruction results are more stable, with higher contrast and sharper local details, demonstrating the effectiveness of the proposed network model.