HoloPASWIN: Robust Inline Holographic Reconstruction via Physics-Aware Swin Transformers

TL;DR

HoloPASWIN is a physics-aware deep learning model using Swin Transformers that significantly improves inline holographic reconstruction by effectively suppressing twin images and capturing global diffraction patterns.

Contribution

The paper introduces HoloPASWIN, a novel transformer-based framework that combines hierarchical attention and physical constraints for robust holographic image reconstruction.

Findings

Effective twin-image suppression demonstrated.

High spectral fidelity achieved in reconstructions.

Robust performance across diverse noise conditions.

Abstract

In-line digital holography (DIH) is a widely used lensless imaging technique, valued for its simplicity and capability to image samples at high throughput. However, capturing only intensity of the interference pattern during the recording process gives rise to some unwanted terms such as cross-term and twin-image. The cross-term can be suppressed by adjusting the intensity of reference wave, but the twin-image problem remains. The twin-image is a spectral artifact that superimposes a defocused conjugate wave onto the reconstructed object, severely degrading image quality. While deep learning has recently emerged as a powerful tool for phase retrieval, traditional Convolutional Neural Networks (CNNs) are limited by their local receptive fields, making them less effective at capturing the global diffraction patterns inherent in holography. In this study, we introduce HoloPASWIN, a physics-aware deep learning framework based on the Swin Transformer architecture. By leveraging hierarchical shifted-window attention, our model efficiently captures both local details and long-range dependencies essential for accurate holographic reconstruction. We propose a comprehensive loss function that integrates frequency-domain constraints with physical consistency via a differentiable angular spectrum propagator, ensuring high spectral fidelity. Validated on a large-scale synthetic dataset of 25,000 samples with diverse noise configurations (speckle, shot, read, and dark noise), HoloPASWIN demonstrates effective twin-image suppression and robust reconstruction quality.