H2HSR: Hologram-to-Hologram Super-Resolution With Deep Neural Network

Abstract

In holography, the resolution of the hologram significantly impacts both display size and angle-of-view, yet achieving high-resolution holograms presents formidable challenges, whether in capturing real-world holograms or in the computational demands of Computer-Generated Holography. To overcome this challenge, we introduce an innovative Hologram-to-Hologram Super-Resolution network (H2HSR) powered by deep learning. Our encoder-decoder architecture, featuring a novel up-sampling block in the decoder, is adaptable to diverse backbone networks. Employing two critical loss functions, data fidelity and perceptual loss, we guide H2HSR to attain pixel-wise accuracy and perceptual quality. Rigorous evaluations, using the MIT-CGH-4K dataset, demonstrate H2HSR’s consistent superiority over conventional interpolation methods and a prior GAN-based approach. Particularly, in conjunction with the SwinIR encoder, H2HSR achieves a remarkable 8.46% PSNR enhancement and a 9.30% SSIM increase compared to the previous GAN-based method. Also, we found that our H2HSR shows more stable reconstruction quality across varying focal distances. These results demonstrate the robustness and effectiveness of our H2HSR in the context of hologram super-resolution.

Keywords

• Holography
• deep learning
• super-resolution
• hologram super-resolution