HoloPASWIN

Robust Inline Holographic Reconstruction via Physics-Aware Swin Transformers

HoloPASWIN recovers clean phase and amplitude mappings from a single intensity hologram, directly eliminating twin-image artifacts.

Overview

🤗 Try the interactive Web Demo on Hugging Face Spaces: gokhankocmarli/holopaswin-v3-space

HoloPASWIN is a deep learning framework designed to eliminate the twin-image artifact in inline digital holography. While inline holography is an effective lensless imaging technique, the loss of phase information during capturing causes an out-of-focus duplicate (twin-image) to permanently degrade the reconstructed object.

This repository implements a Physics-Aware Swin Transformer U-Net that inherently corrects and removes these artifacts. By integrating a forward physics model (the Angular Spectrum Method) with the Swin Transformer's global attention, HoloPASWIN achieves robust phase recovery and high structural fidelity across varying noise levels and distances.

Network Architecture

HoloPASWIN utilizes a U-shaped architecture based on Swin Transformer blocks. The model first processes an input intensity hologram using the backward Angular Spectrum Method (ASM) to obtain an initial, artifact-heavy complex field. A 4-stage Swin Encoder-Decoder network then extracts multi-scale features to predict a residual correction. By adding this correction to the initial field and training with both frequency-domain constraints and a physics-based forward propagation loss, the network robustly recovers the clean phase and amplitude.

Installation

This project uses uv for fast and reliable dependency management.

1. Install uv (if not already installed):

   curl -LsSf https://astral.sh/uv/install.sh | sh

2. Sync Dependencies: Navigate to the holopaswin directory and run:

   uv sync

   This creates a virtual environment and installs all locked dependencies from uv.lock.

Usage

Training

To train the model from scratch on the dataset, run:

uv run src/train.py

Inference & Testing

To evaluate a trained model and generate visualization results on test samples, run:

uv run src/inference.py

To calculate full quantitative metrics (SSIM, PSNR, MSE) over the test dataset, run:

uv run src/test.py

Development

This repository includes pre-commit hooks that automatically run code quality checks (ruff and mypy) before each commit. To install them:

./scripts/install-hooks.sh

If any check fails, the commit will be blocked.

Citation

If you find this code, dataset, or model useful for your research, please cite our paper:

@misc{kocmarli2026holopaswinrobustinlineholographic,
      title={HoloPASWIN: Robust Inline Holographic Reconstruction via Physics-Aware Swin Transformers}, 
      author={Gökhan Koçmarlı and G. Bora Esmer},
      year={2026},
      eprint={2603.04926},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2603.04926}, 
}