Sparse holographic tomography reconstruction method based on self-supervised neural network with learning to synthesize strategy

Abstract

This research proposes a novel method for sparse digital holographic tomography reconstruction. Due to the limitations of numerical aperture and sampling time, the development of a high-precision sparse digital holographic tomography reconstruction techniques is necessitated. Our main innovation is the developing a composite coordinate-based implicit neural network with learning to synthesize strategy. It addresses the information limitations of limited angle by directly mapping the sample’s rotation angle and coordinates to the phase images, allowing for the prediction of phase images at unmeasured angles without requiring external training dataset. Furthermore, it avoids the issue of high-frequency suppression caused by the uneven distribution of frequency information in the images and the network’s characteristics using separately processing low-frequency and high-frequency information in different channels, resulting in higher fidelity of the predicted phase images and the tomographic results. We validated the effectiveness of the proposed method on four different fiber structures at various sampling intervals. This method provides a new perspective for tomographic reconstruction at sparse angles.