holopaswin-v3

HoloPASWIN v3

HoloPASWIN (Physics-Aware Swin Transformer for Holographic Reconstruction) is a state-of-the-art model for eliminating twin-image artifacts and recovering complex object fields (amplitude and phase) from single-shot in-line holograms.

Architecture

The model combines the exact physical model of free-space propagation with the powerful representation learning of a Swin Transformer.

1. Physics Module: The input hologram intensity is linearly back-propagated to the object plane using the Angular Spectrum Method (ASM). This creates a "dirty" complex field that contains the focused object but is corrupted by the defocused out-of-focus twin image.
2. Swin Transformer Encoder-Decoder: The dirty field (split into real and imaginary parts) is passed through a hierarchical Swin Transformer U-Net architecture.
3. Residual Correction: Instead of predicting the clean field directly, the network predicts the twin-image artifacts. These are subtracted (via a residual connection) from the dirty input to yield the final, high-fidelity clean reconstruction.

Model Details

• Input: 1-channel Hologram Intensity (B, 1, H, W)
• Output: 2-channel Complex Field (Real, Imaginary) mapped to Amplitude and Phase (B, 2, H, W)
• Encoder Stages: 4 stages with channels [96, 192, 384, 768]
• Parameters: ~30.88M
• Inference Time: ~13.7 ms per image (on modern GPU)

Advancements

HoloPASWIN v3 improves upon traditional CNN-based approaches and iterative solvers:

• Physics-Informed: By letting an exact physical solver handle the propagation mapping, the neural network only has to learn the artifact removal, greatly simplifying the learning objective.
• Global Receptive Field: The Swin Transformer backbone captures long-range spatial dependencies critical in holography, where twin-image interference spreads globally across the entire field of view.
• Single-Shot: Unlike iterative Phase Retrieval algorithms (like Gerchberg-Saxton) that can take hundreds of iterations, this method recovers the field in a single forward pass (~13.7ms).

Outcomes and Results

Evaluated on a test set of 496 holographic samples, HoloPASWIN v3 achieves excellent reconstruction fidelity in both amplitude and phase domains.

Qualitative Results

Below are sample reconstructions showing the Ground-Truth arrays juxtaposed against the Ground-Truth Amplitude & Phase, Zoomed Details, and Error Maps.

Quantitative Metrics

Note: While a significantly larger classic U-Net baseline achieves slightly higher raw SSIM metrics on this particular dataset split, HoloPASWIN operates twice as fast (13.7ms vs 23.1ms) while avoiding the common over-smoothing associated with standard CNNs.

How to use

You can load the required classes directly from the repository.

from huggingface_hub import hf_hub_download
import torch

# Load the weights
model_path = hf_hub_download(repo_id="gokhankocmarli/holopaswin-v3", filename="pytorch_model.bin")

# Import the model architecture provided in the same repo
# (Ensure src/holopaswin is in your PYTHONPATH)
from holopaswin.model import HoloPASWIN

# Initialize model
model = HoloPASWIN(
    img_size=224, 
    in_chans=1,
    embed_dim=96,
    depths=[2, 2, 6, 2],
    num_heads=[3, 6, 12, 24],
    window_size=7,
)

# Load state dict
model.load_state_dict(torch.load(model_path))
model.eval()

# Dummy input
dummy_hologram = torch.randn(1, 1, 224, 224)
with torch.no_grad():
    clean_real_imag = model(dummy_hologram)

Citation

If you find this work useful, please cite our paper:

@misc{kocmarli2026holopaswin,
      title={HoloPASWIN: Robust Inline Holographic Reconstruction via Physics-Aware Swin Transformers}, 
      author={Gokhan Kocmarli},
      year={2026},
      eprint={2603.04926},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

Or view the paper directly: HoloPASWIN: Robust Inline Holographic Reconstruction via Physics-Aware Swin Transformers