13 results found Sort:

483
1.1k
bsd-3-clause
47
Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks. Support: https://discourse.slicer.org/c/community/radiomics
Created 2015-09-15
1,230 commits to master branch, last one about a month ago
Resources for phase recovery (also called phase imaging, phase retrieval, or phase reconstruction)
Created 2023-05-31
200 commits to main branch, last one about a month ago
157
288
bsd-3-clause
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BART: Toolbox for Computational Magnetic Resonance Imaging
Created 2014-08-22
3,397 commits to master branch, last one 4 days ago
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237
bsd-3-clause
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PyTorch library for solving imaging inverse problems using deep learning
Created 2023-02-10
905 commits to main branch, last one 19 hours ago
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mpl-2.0
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Scientific computing library for optics, computer graphics and visual perception
Created 2012-04-10
1,644 commits to master branch, last one 24 hours ago
A curated list of resources on holographic displays.
Created 2022-03-05
39 commits to main branch, last one about a year ago
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bsd-3-clause
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Scientific Computational Imaging COde
Created 2021-09-21
883 commits to main branch, last one 3 days ago
Official Demo Code for "Unlocking the Performance of Proximity Sensors by Utilizing Transient Histograms"
Created 2023-11-11
6 commits to main branch, last one 28 days ago
(Tensorflow Version) D-Flat is a forward and inverse design framework for flat optics. Although specially geared for the design of metasurface optics, it may be used for any end-to-end imaging and sen...
Created 2022-07-13
360 commits to main branch, last one 4 months ago
A Julia project demonstrating the fast f-k migration algorithm.
Created 2019-08-20
3 commits to master branch, last one 3 years ago
8
49
unknown
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DFlat is a forward and inverse design framework for flat optics. Although specially geared for the design of metasurface optics, it may be used for any end-to-end imaging and sensing task.
Created 2023-07-14
121 commits to main branch, last one 23 days ago
Image-to-image regression with uncertainty quantification in PyTorch. Take any dataset and train a model to regress images to images with rigorous, distribution-free uncertainty quantification.
Created 2021-05-30
152 commits to main branch, last one about a year ago