# Holopaswin Reconstruction Eval

> Evaluates a deep learning model's ability to reconstruct complex object fields from in-line digital holograms, specifically testing its capacity to suppress twin-image artifacts and maintain reconstruction fidelity under various noise conditions. Use when the user wants to benchmark on Synthetic Inline Holography Dataset, or asks about evaluating this task. Reports reconstruction fidelity.

- Skill: `qhjqhj00/holopaswin-reconstruction-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/holopaswin-reconstruction-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/holopaswin-reconstruction-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/holopaswin-reconstruction-eval

---


# holopaswin-reconstruction-eval

> HoloPASWIN: Robust Inline Holographic Reconstruction via Physics-Aware Swin Transformers — Kocmarli et al. (2026) (arXiv:2603.04926, 2026)

## What this evaluates

Evaluates a deep learning model's ability to reconstruct complex object fields from in-line digital holograms, specifically testing its capacity to suppress twin-image artifacts and maintain reconstruction fidelity under various noise conditions.

## Datasets

- **Synthetic Inline Holography Dataset** — total 25496; splits: train (20000), val (5000), test (496); repo https://github.com/electricalgorithm/holopaswin

## Metrics

- `reconstruction fidelity` **(primary)** — range: other
  - Not explicitly defined in the provided section; generally refers to quantitative comparison between the predicted complex object field and the ground-truth object field.
- `twin-image suppression` — range: other
  - Not explicitly defined in the provided section; evaluates the model's ability to eliminate conjugate-free twin artifacts inherent to phase-loss in intensity recording.

## Input / output format

**Input**: 224×224 pixel intensity hologram, normalized by dividing raw 12-bit intensity values by 1000.0.

**Output**: 224×224 pixel complex object field (real and imaginary components), left unnormalized.

## Scoring recipe

```python
pred_field = model.predict(normalized_hologram)
gt_field = load_ground_truth_object_field()
fidelity_score = compute_fidelity_metric(pred_field, gt_field)
twin_suppression_score = compute_twin_suppression_metric(pred_field, gt_field)
return fidelity_score, twin_suppression_score
```

## Common pitfalls

- The dataset is entirely synthetic; the authors explicitly state that validation on experimental data is left for future work.
- The simulation only models objects at a single depth plane (z=0) and does not account for volumetric scattering.
- Twin-image artifacts arise purely from phase-loss during intensity recording, not from out-of-focus objects, which is a common misconception in holography.

## Evidence (verbatim from paper)

> outperforming CNN-based methods in both twin-image suppression and reconstruction fidelity. A separate external test set of 496 samples was used for final evaluation. The supervised loss and physics consistency loss together guide the network to produce a physically valid solution that eliminates the twin artifact.

## Citation

```bibtex
@misc{kochmarli2026holopaswin,
  title={HoloPASWIN: Robust Inline Holographic Reconstruction via Physics-Aware Swin Transformers},
  author={Kocmarli et al. (2026)},
  year={2026},
  note={arXiv:2603.04926}
}
```

- arXiv: 2603.04926