📝 SummarXiv

Abstract

In-line holography offers high space-bandwidth product imaging with a simplified lens-free optical system. However, in-line holographic reconstruction is troubled by twin images arising from the Hermitian symmetry of complex fields. Twin images disrupt the reconstruction in solving the ill-posed phase retrieval problem. The known parameters are less than the unknown parameters, causing phase ambiguities. State-of-the-art deep-learning or non-learning methods face challenges in balancing data fidelity with twin-image disturbance. We propose the Gaussian splatting holography (GSH) for twin-image-suppressed holographic reconstruction. GSH uses Gaussian splatting for optical field representation and compresses the number of unknown parameters by a maximum of 15 folds, transforming the original ill-posed phase retrieval into a well-posed one with reduced phase ambiguities. Additionally, the Gaussian splatting tends to form sharp patterns rather than those with noisy twin-image backgrounds as each Gaussian has a spatially slow-varying profile. Experiments show that GSH achieves constraint-free recovery for in-line holography with accuracy comparable to state-of-the-art constraint-based methods, with an average peak signal-to-noise ratio equal to 26 dB, and structure similarity equal to 0.8. Combined with total variation, GSH can be further improved, obtaining a peak signal-to-noise ratio of 31 dB, and a high compression ability of up to 15 folds.