Ps-ViT: phase space vision transformer pre-training for the depth estimation in computer-generated holograms

Recent advances in neural network pre-training have significantly improved state-of-the-art performance across various computer vision tasks, especially in scenarios with limited labeled data. These improvements stem from the ability to learn transferable and robust image feature descriptors from large-scale, unlabeled, and often noisy datasets through self-supervised training. Despite these successes, the field of holography has seen limited benefits from such approaches due to the challenges in developing effective pre-training strategies tailored to holographic data. In this work, we address this gap by introducing a pre-training method leveraging the hologram phase space representation. This approach enables the learning of efficient feature descriptors optimized for dense depth map estimation, unlocking new potential in holographic imaging applications.