Sarepy
(S)tripe (A)rtifacts (RE)moval in (PY)thon
Numerical techniques for ring artifact removal in X-ray micro-tomography
Sarepy is the Python implementations of methods for removing ring artifacts in tomography. These implementations were published for the paper; "Superior techniques for eliminating ring artifacts in X-ray micro-tomography," Optics Express, 26, 28396-28412 (2018): https://doi.org/10.1364/OE.26.028396.
Sarepy includes implementations of several former methods and post-processing methods.
Author:
Nghia Vo - NSLS-II, Brookhaven National Lab, US; Diamond Light Source, UK
Matlab implementation contributor:
Daniel S. Hussey - NIST(National Institute of Standards and Technology), US
Important notice
Starting 05/2021, methods in Sarepy have been integrated and developed further in the Algotom package, https://github.com/algotom/algotom . Algotom is a complete package for processing tomographic data. It is installable using Conda and Pip.
Many utility methods were added to Algotom which allow users to customize stripe/ring removal methods as demonstrated here.
Comparison of using different removal methods on challenging sinograms is shown here
How to use
Clone or download the codes to your local machine, then insert the following two lines to your python codes:
import sys
sys.path.insert(0, "path-to-sarepy-pck")Making sure that the python libs in the requirements.txt are installed before use.
Details of how to use the methods can be found in /examples/examples.py. Noting that
parameters chosen in these examples are for sinograms in the /data folder.
The selected windows of the median filter (81 for large stripes, 31 for others)
may be overkill for good quality detectors. You should change these parameters
to suit your data.
Output of methods is 32-bit data. A viewer software which can display 32-bit tiff image is needed, e.g. ImageJ or Fiji.
Features
- Methods for cleaning different types of stripe artifacts: full stripes, partial stripes, unresponsive stripes, fluctuating stripes, large stripes, and blurry stripes.
- Various approaches based on the equalization-based methods, i.e equalizing the "response curves" of adjacent pixels, and their combinations.
- A robust stripe detection method.
- Implementations of former methods: a regularization-based method, a normalization-based method, a fft-based method, and a wavelet-fft-based method.
- Matlab implementations of algorithms 3,4,5,6.
- Implementations of a basic pipeline of tomography reconstruction: data loading, automated determination of center of rotation, ring artifact removal, tomographic reconstruction, and data saving.
- Postprocessing methods for removing ring artifacts: polar transformation, fft-based methods.
Documentation
Update Notes
- 09/10/2019:
Add methods for loading and saving data, methods for calculating center of rotation, wrappers of reconstruction methods (from tomopy and astra). This allows users to try or test ring removal methods easily. - 11/02/2020:
Allow to use 2D kernel in the median filter of the sorting-based correction methods. This is done based on feedbacks from neutron imaging users. Note that this increases the computational cost.
Add sinograms for testing. - 05/05/2020:
Publish documentation on readthedocs.
Add postprocessing methods. - 15/09/2020:
Add an interpolation-based stripe removal method. It is a combination of algorithm 4, 5, and 6 in https://doi.org/10.1364/OE.26.028396 - 05/2021:
Integrate and maintain codes in the Algotom package.