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猫爱吃鱼 / image_quality_assessmentPythonMIT

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Indexs for image quality assessment, mainly for Emission Tomography spread retract

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README.md

Image Quality Assessment

test

Brief description

This repository contains various methods to assess the quality of an image and to construct simulated dataset to test tomographic reconstruction algorithms.

The following metrics are included (Python or MATLAB):

  • Tested

    • Signal-to-Noise-Ratio (SNR).
    • Peak-Signal-to-Noise-Ratio (PSNR).
    • Root-Mean-Squared-Error (RMSE).
    • Mean-Absolute-Error (MAE)
    • Structural Similarity Index (SSIM).
  • TO Test

    • Normalized Mutual Information (NMI).
    • Image Complexity.
    • Resolution analysis through Edge-Profile-Fitting (EPF).
    • Resolution analysis through Fourier Ring Correlation (FRC).

The following routines to construct simulated datasets are included:

  • Create a Shepp-Logan phantom.

  • Create generic phantoms with analytical X-ray transform.

  • Rescale image.

  • Downsample sinogram.

  • Add Gaussian or Poisson noise.

  • Add Gaussian blurring.

Requirements

  • Python3

    • scipy
    • scikit-image
    • PIL
    • h5py
  • MATLAB

Test the package

Go inside the folder "data/" and unzip the test dataset: unzip dataset.zip.

Then, inside the folder "tests/" try to run one by one the test scripts.

When a plot is produced during the execution of a test, the script is halted until the plot window is manually closed.

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