gbuzzard/PnP-MACE
Utilities and methods to use the PnP algorithm and MACE framework on image reconstruction problems. Includes demos for superresolution and CT.
This helps scientists and engineers improve the clarity of images by reconstructing them from incomplete or noisy data. You input a low-quality image or partial image data, and it outputs a significantly enhanced, higher-resolution image. Researchers in medical imaging, materials science, and remote sensing would find this useful for tasks like superresolution and CT reconstruction.
No commits in the last 6 months. Available on PyPI.
Use this if you need to reconstruct high-quality images from limited measurements, such as making a blurry image sharper or building a complete image from only a few scans.
Not ideal if you are looking for general-purpose image editing, object recognition, or tasks unrelated to reconstructing images from insufficient data.
Stars
19
Forks
5
Language
Python
License
—
Category
Last pushed
Feb 12, 2024
Commits (30d)
0
Dependencies
6
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