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High resolution single pixel imaging

Version 2.0

Sobczak, Kacper; Kotyński, Rafał; Stojek, Rafał; Pastuszczak, Anna; Cwojdzińska, Magdalena; Wróbel, Piotr, 2024, "High resolution single pixel imaging", https://doi.org/10.58132/GTDMO6, Dane Badawcze UW, V2

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Description

This dataset contains the data used to produce the results from the manuscript:

Stojek, R.; Pastuszczak, A.; Wróbel, P.; Cwojdzińska, M.; Sobczak, K.; Kotyński, R.: "High-Resolution Single-Pixel Imaging of Spatially Sparse Objects: Real-Time Imaging in the Near-Infrared and Visible Wavelength Ranges Enhanced with Iterative Processing or Deep Learning". Preprints2024, 2024111153. https://doi.org/10.20944/preprints202411.1153.v1

The data includes image maps, the measurement matrix with binary sensing patterns and the generalized inverse of the measurement matrix for single pixel imaging at the native resolution of the DMD, i.e. 1024 x 768 pixels.

It also includes the trained model of the neural network, which has been used in the manuscript to enhance the image reconstruction, with a portion of a Python code which enables loading all the necessary data, performing simulated single-pixel measurement and the initial reconstruction with the provided measurement matrix and its inverse, and finally performing the 2-nd stage reconstruction with the neural network and visualizing the results. We also include 2 sets of handcrafted testing images used in the above manuscript, each containing 5000 images with resolution 1024 x 768 of either high-resolution binary line-art or modified MNIST dataset.

(2024-10-24)
Subject
Physics
Keyword

single-pixel imaging, high-resolution, compressed sensing, image reconstruction

Related Publication

Stojek, R.; Pastuszczak, A.; Wróbel, P.; Cwojdzińska, M.; Sobczak, K.; Kotyński, R.: "High-Resolution Single-Pixel Imaging of Spatially Sparse Objects: Real-Time Imaging in the Near-Infrared and Visible Wavelength Ranges Enhanced with Iterative Processing or Deep Learning". Preprints2024, 2024111153. https://doi.org/10.20944/preprints202411.1153.v1 https://doi.org/10.20944/preprints202411.1153.v1 doi: 10.20944/preprints202411.1153.v1

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map.npy
Unknown - 75.0 MB - Oct 30, 2024 - 1 Download
MD5: 78c0974dee50ff90df196f82ff4ebb82
License: CC BY - Creative Commons Attribution 4.0
Matrix containing as its rows a set of 100 image maps with 31 regions in each map.
Data
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map_patterns_packed.npy
Unknown - 300.0 MB - Oct 30, 2024 - 1 Download
MD5: fc3ba12a4c988c33db50d2cd759b9214
License: CC BY - Creative Commons Attribution 4.0
Measurement matrix containing 3200 binary sensing patterns with resolution 768 x 1024 for compressive single-pixel imaging.
Data
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map_inverse.zip.1
ZIP Archive - 5.0 GB - Oct 30, 2024 - 0 Downloads
MD5: c2ec6e1800db9a824fc2492571ed79e8
License: CC BY - Creative Commons Attribution 4.0
Generalized inverse of the measurement matrix (part 1).
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map_inverse.zip.2
Unknown - 3.6 GB - Oct 30, 2024 - 0 Downloads
MD5: 0969a0da536fe77afc8d9dd2edbb0fb0
License: CC BY - Creative Commons Attribution 4.0
Generalized inverse of the measurement matrix (part 2).
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map_decrypt.npy
Unknown - 39.1 MB - Dec 6, 2024 - 0 Downloads
MD5: a94c76edff0354c2841a252ce239c3e0
License: CC BY - Creative Commons Attribution 4.0
Decription array W. In enables calculatating sums of pixels in each region of every image map directly from the SPI measurement vector y=Mx, even without initial image reconstruction.
Data
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Denoiser_768x1024.pt
ZIP Archive - 1.0 MB - Dec 6, 2024 - 1 Download
MD5: 62bc9005a30fc2875677148a8821909e
License: CC BY-SA Creative Commons Attribution - ShareAlike 4.0
Neural network trained parameters for second-stage high resolution SPI image reconstruction.
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LineArt_Testset.npy
Unknown - 3.7 GB - Dec 6, 2024 - 0 Downloads
MD5: 8a35c43f4aa4a495a92edf853d882672
License: CC BY - Creative Commons Attribution 4.0
Testing data set containing 5000 high-resolution binary line-art images, each with 768 x 1024 pixels.
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Numbers_Testset.npy
Unknown - 3.7 GB - Dec 6, 2024 - 0 Downloads
MD5: a01bdeb41ae322575602a80c989d1c7f
License: CC BY - Creative Commons Attribution 4.0
Testing data set containing 5000 grayscale images, each with 768 x 1024 pixels, composed of multiple randomly placed and resized handwritten digits from MNIST data set.
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NN_SPI_demonstration.py
Unknown - 8.1 KB - Dec 6, 2024 - 2 Downloads
MD5: a22c4987e8e00777e5845c6faa53ac9b
License: CC BY-SA Creative Commons Attribution - ShareAlike 4.0
Demonstrative code showing the performance of compressive SPI imaging with sampling based on image maps and reconstruction assisted with a neural network.
Code
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