gokhankocmarli/inline-digital-holography

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, which provides simulation and learning tools for inline holography.

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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 | GitHub | LinkedIn
• Funded by [optional]: Independent research project
• License: MIT
• Repository: 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.

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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).

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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:

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.

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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 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.

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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.

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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).

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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.

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Citation

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.}
}