---
license: mit
task_categories:
- image-to-image
- image-segmentation
- image-classification
- image-feature-extraction
- zero-shot-image-classification
tags:
- holography
- hologram
- inline-holography
- inline
- reconstruction
- synthetic
- signal
pretty_name: Synthetic Inline Holographical Images
size_categories:
- 100K<n<1M
---

# Dataset Card for Synthetic Inline Holographical Images

This dataset provides synthetic image triplets representing **inline holographical imaging** in a simulated environment. Each data sample consists of:
1. An **object-domain image** (the ground-truth object or amplitude pattern),
2. Its corresponding **forward-propagated hologram** (the inline holographic pattern at the sensor plane),
3. The **numerically reconstructed image** (via back-propagation or angular spectrum method).

The dataset is intended to facilitate research in **computational imaging, holographic reconstruction, phase retrieval**, and **machine learning-based hologram analysis**.

It is primarily used in conjunction with the open-source project [**Hologen**](https://github.com/electricalgorithm/hologen), which provides simulation and learning tools for inline holography.

![Example Data](https://github.com/electricalgorithm/hologen/blob/main/docs/example_comparison.png?raw=true)


---

## Dataset Details

The **Synthetic Inline Holographical Images** dataset contains triplets of images generated from numerically simulated optical propagation.  
The synthetic nature of the data enables large-scale, controllable experiments without the need for physical holographic recording setups.  
This makes the dataset especially suitable for deep learning research in holographic imaging, where paired data (object ↔ hologram ↔ reconstruction) are rarely available.

- **Curated and shared by:** Gökhan Koçmarlı (Gyokhan Kochmarla)[Google Scholar](https://scholar.google.com/citations?hl=en&user=VbZdKZ4AAAAJ) | [GitHub](https://github.com/electricalgorithm/) | [LinkedIn](https://linkedin.com/in/gokhankocmarli/)
- **Funded by [optional]:** Independent research project
- **License:** MIT
- **Repository:** [https://github.com/electricalgorithm/hologen](https://github.com/electricalgorithm/hologen)
- **Data Amount:** 100000 Samples
- **Compressed Size:** 93.7GB
- **Raw Size:** 157.4GB


### Simulation Settings

One can reproduce the results using the HoloGen Toolkit with following simulation settings.

| Parameter | Description | Value |
|------------|--------------|-------|
| **Simulation seed** | Random number generator's seed | 42 |
| **Object height** | Height of both object and sensor plane | 256 pixels |
| **Object width** | Width of both object and sensor plane | 256 pixels |
| **Pixel pitch** | Physical spacing between adjacent pixels | 4.65e-6 meters |
| **Illumination wavelength** | Monochromatic light wavelength | 532e-9 meters |
| **Propagation distance** | Distance between object and sensor planes | 0.02 meters |

The data does not contain any noise or aberration.

---

## Uses

The dataset is designed for:
- Training and evaluating neural networks that reconstruct objects from inline holograms.  
- Developing models for **phase retrieval**, **complex field estimation**, and **denoising**.  
- Exploring **signal transformation relationships** between object and propagation domains.  
- Benchmarking holographic forward and inverse modelling algorithms.  

This dataset is **not suitable** for:
- Real-world holography generalisation studies without domain adaptation.  
- Tasks requiring physical measurements or phase-accurate calibration data.  
- Medical, biometric, or personal data analysis (no human-related content is included).  

---

## Dataset Structure

Each data point contains a NPZ file which holds three images:
- `object` – Grayscale object-domain amplitude or intensity image.
- `hologram` – Forward-propagated inline hologram (synthetic sensor-plane recording).
- `reconstruction` – Numerically reconstructed image from the hologram.

Example:
```py
import numpy as np
from pathlib import Path

# Path to the NPZ file
npz_file = Path("sample_09018_rectangle_checker.npz")

# Load the NPZ file
with np.load(npz_file) as data:
    object_img = data["object"]
    hologram_img = data["hologram"]
    reconstruction_img = data["reconstruction"]
```

The shapes included in the dataset as follows:

- `circle`: min_radius=0.08, max_radius=0.18
- `rectangle`: min_scale=0.1, max_scale=0.35 
- `ring`: min_radius=0.12, max_radius=0.25, min_thickness=0.1, max_thickness=0.3
- `circle_checker`: min_radius=0.1, max_radius=0.2, checker_size=16
- `rectangle_checker`: min_scale=0.1, max_scale=0.35, checker_size=16
- `ellipse_checker`: min_radius_y=0.1, max_radius_y=0.35, min_radius_x=0.1, max_radius_x=0.35, checker_size=16,

Image dimensions and bit depth are consistent across domains.

---

## Dataset Creation

### Curation Rationale

Inline holography involves recording the interference pattern between an object wave and a reference wave. However, collecting large, labelled datasets in laboratory conditions is impractical due to optical setup complexity and noise factors. This dataset provides a **synthetic, physically consistent alternative** that mimics realistic propagation physics using scalar diffraction models.

### Source Data

#### Data Collection and Processing

Images were generated using numerical wave propagation based on the **Huygens–Fresnel principle** or **Angular Spectrum Method (ASM)**, as implemented in the [Hologen](https://github.com/electricalgorithm/hologen) framework. Objects were synthetically generated using shape primitives, textures, and random amplitude patterns. Each object was propagated through a simulated inline holography setup to produce hologram and reconstruction pairs.

#### Who are the source data producers?

- All data were generated algorithmically by Gökhan Koçmarlı using simulation code in *Hologen*.  
- No external or third-party datasets were used.

---

### Annotations

No manual annotations are included.
Each triplet is automatically labelled by filename correspondence.

#### Annotation process

Not applicable (fully synthetic, self-labelled data).  

#### Who are the annotators?

All data is generated programmatically.

#### Personal and Sensitive Information

This dataset contains **no personal, identifiable, or sensitive information**.  
All images are synthetic and algorithmically generated.

---

## Bias, Risks, and Limitations

- As the dataset is fully synthetic, it lacks real-world optical aberrations, noise, and coherence effects that occur in experimental holography.  
- Models trained purely on this dataset may require fine-tuning on physical hologram data to generalise effectively.  
- The dataset assumes ideal optical parameters (e.g., monochromatic light, planar sensor).

---

### Recommendations

Users should consider:
- Augmenting with noise or real holograms for domain adaptation.  
- Interpreting reconstruction metrics (e.g., PSNR, SSIM) relative to synthetic references.  
- Avoiding conclusions about physical accuracy without experimental validation.

---

## Citation

**BibTeX:**
```bibtex
@dataset{kochmarla2025synthetic_inline_holographical_images,
  author       = {Gökhan Koçmarlı},
  title        = {Synthetic Inline Holographical Images},
  year         = {2025},
  url          = {https://huggingface.co/datasets/electricalgorithm/nline-digital-holography},
  note         = {Synthetic dataset for inline holography simulation and reconstruction.}
}