SerPy-X 0.1.3

Statistical single-exposure retrieval of transmission, isotropic and directional dark-field signals for X-ray imaging

🌀 SerPy — Statistical single-exposure Retrieval of directional dark-field in PYthon

SerPy implements a statistical single-exposure framework for retrieving transmission, isotropic dark-field, and directional dark-field (DF) signals from a single pair of images (reference and sample).
It is compatible with both modulation-based, speckle-based, single grid, edge illumination, and grating-based X-ray imaging techniques.

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🚀 Overview

Conventional dark-field imaging requires multiple phase steps or modulator positions to separate phase, absorption, and scattering information.
SerPy eliminates this need by relying on local contrast statistics computed within small sliding windows.
From these statistics, the method reconstructs the directional scattering ellipse at each pixel, yielding quantitative maps of:

• Transmission $ T(\mathbf{r})$
• Isotropic dark-field $ D_f(\mathbf{r}) $
• Directional dark-field: orientation, anisotropy, and magnitude

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✳️ Key Features

• 🔹 Single-exposure retrieval — no phase stepping or mask motion required
• 🔹 Local statistical analysis of contrast anisotropy
• 🔹 Directional dark-field extraction via ellipse fitting in polar space
• 🔹 Orientation smoothing using circular Gaussian filtering
• 🔹 Vectorized & Numba-accelerated kernels for high performance
• 🔹 Compatible with modulation-based, speckle-based, single grid, edge illumination, grating-based setups

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⚙️ Installation

▶️ From PyPI (recommended)

pip install serpy_x

▶️ From source

git clone https://github.com/MuguiwaraSamy/SerPy.git
cd SerPy
pip install -U pip
pip install -e .

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🧠 Quickstart Example

import numpy as np
from Serpy.statdf import retrieval_Algorithm

# img, ref : 2D numpy arrays (sample and reference)
out = retrieval_Algorithm(img, ref, n_angles=19, window_size=5)

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Key	Description
angles	Array of sampled orientation angles (in radians) used for the directional analysis
T	Transmission map ( T(\mathbf{r}) )
Non-oriented Df	Isotropic dark-field (scalar visibility loss)
Oriented Df	Directional dark-field profiles sampled along each orientation ((H, W, N_\theta))
major axis	Semi-major axis of the fitted ellipse (maximum scattering direction)
minor axis	Semi-minor axis of the fitted ellipse (minimum scattering direction)
Orientation	Raw ellipse orientation (principal scattering direction, before smoothing)
Corrected Orientation	Smoothed and π-periodic orientation map
saturation	Local angular coherence or stability of the orientation field
(optionally) mean_s, std_s, mean_r, std_r	Intermediate directional statistics (sample/reference means and standard deviations), included if return_intermediates=True

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⚠️ Patent Notice

A patent application covering the underlying method and algorithm has been filed by the author(s): "Procédé d’imagerie et produit programme d’ordinateur correspondant", filed on September 26, 2025.

The publication of this code under the MIT license does not grant any license or rights to practice the patented invention.
Use of this software for research and non-commercial purposes is permitted under the terms of the MIT License.
Any commercial use of the patented method requires a separate license from the patent holder.

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📜 License

Distributed under the MIT License. © 2025 Samy Kefs. All rights reserved.

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