RAISR
A Python implementation of RAISR. Supporting color images.
How To Use
Prerequisites
You can install most of the following packages using pip.
Training
Put your training images in the train directory, and run the following command to start training.
python train.py
The learned filters will be saved in the root directory of the project.
Testing
Put your testing images in the test directory, and run the following command to start generating high resolution images using RAISR.
python test.py
The result will be saved in the results directory.
Visualization
Visualing the learned filters
Uncomment the following line in train.py.
# filterplot(h, R, Qangle, Qstrength, Qcoherence, patchsize)
Visualing the process of RAISR image upscaling
Uncomment the following line in test.py.
# plt.show()
Testing Results
Comparing between original image, bilinear interpolation and RAISR:
| Origin | Bilinear Interpolation | RAISR |
|---|---|---|
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Other results using images taken from BSDS500 database and ArTe-Lab 1D Medium Barcode Dataset:
| Origin | RAISR |
|---|---|
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References
- Y. Romano, J. Isidoro and P. Milanfar, "RAISR: Rapid and Accurate Image Super Resolution" in IEEE Transactions on Computational Imaging, vol. 3, no. 1, pp. 110-125, March 2017.
- P. Arbelaez, M. Maire, C. Fowlkes and J. Malik, "Contour Detection and Hierarchical Image Segmentation", IEEE TPAMI, Vol. 33, No. 5, pp. 898-916, May 2011.
- Alessandro Zamberletti, Ignazio Gallo and Simone Albertini, "Robust Angle Invariant 1D Barcode Detection", Proceedings of the 2nd Asian Conference on Pattern Recognition (ACPR), Okinawa, Japan, 2013
License
MIT. Copyright (c) 2017 James Chen