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JalaliLabUCLA / repository
Implementation of RAISR (Rapid and Accurate Image Super Resolution) algorithm in Python 3.x by Jalali Laboratory at UCLA. The implementation presented here achieved performance results that are comparable to that presented in Google's research paper (with less than ± 0.1 dB in PSNR). Just-in-time (JIT) compilation employing JIT numba is used to speed up the Python code. A very parallelized Python code employing multi-processing capabilities is used to speed up the testing process. The code has been tested on GNU/Linux and Mac OS X 10.13.2 platforms.
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RAISR (Rapid and Accurate Image Super Resolution) is an image processing algorithm published by Google Research in 2016. With sufficient training data, consisting of low and high resolution image pairs, RAISR algorithm tries to learn a set of filters which can be applied to an input image that is not in the training set, to produce a higher resolution version of it. The source code released here is the Jalali-Lab implementation of the RAISR algorithm written in Python 3.x by Sifeng He, under the guidance of Prof. Bahram Jalali. The implementation presented here achieved performance results that are comparable to that presented in Google's research paper (with less than ± 0.1 dB in PSNR). Just-in-time (JIT) compilation employing JIT numba is used to speed up the Python code. A very parallelized Python code employing multi-processing capabilities is used to speed up the testing process. The code has been tested on GNU/Linux and Mac OS X 10.13.2 platforms.
All the dependent Python modules needed for the using our RAISR implementation can be installed using pip package manager and are the following:
Additionally, a requirements.txt file is provided for installing all the dependencies by running the following command in a shell:
pip install -U -r requirements.txt
All the training images are to be stored in the trainingData directory, before executing the Train.py script. In the training script, modify the upscaling factor, R, appropriately. The training outputs three files, filter, Qfactor_str, and Qfactor_coh, which are needed in the testing phase. The training data used in this implementation is the BSD 200 (Martin et al. ICCV 2001). A pre-trained filter with upscaling factors x2, x3, and x4 are available for testing in the Filter directory. To execute training, simply run the following command in your shell:
python Train.py
All the test images are to be stored in the testData directory, before executing the Test.py script. In the test script, change the upscaling factor, R, and filters. The result image will be saved in results directory. The test data used for the development of the algorithm were Set 5 (Bevilacqua et al. BMVC 2012) and Set 14 (Zeyde et al. LNCS 2010). To execute testing, simply run the following command in your shell:
python Test.py
| Upscale Factor | Google RAISR | Our implementation trained on BSD 200 images | Our implementation trained on COCO (500 images) |
|---|---|---|---|
| 2x | 35.913 | 35.855 | 35.878 |
| 3x | 32.061 | 31.981 | 32.019 |
| 4x | 29.689 | 29.717 | 29.801 |
| Upscale Factor | Google RAISR | Our implementation trained on BSD 200 images | Our implementation trained on COCO (500 images) |
|---|---|---|---|
| 2x | 31.980 | 31.790 | 31.816 |
| 3x | 28.764 | 31.714 | 28.729 |
| 4x | 26.912 | 26.901 | 26.935 |
Google's RAISR implementation was trained on 10000 advertising banner images. Our implementation of RAISR was trained on BSD 200 (200 images) and on 500 images from COCO dataset. The training time for our implementation on a 3.4 GHz 6-core Xeon desktop computer is about 20 minutes for BSD 200 dataset and an hour for the 500 images from COCO dataset. The average test time for one single image in test dataset (Set 5 and Set 14) is less than 1 second.
Please cite the following publications if you plan to use the code or the results for your research:
Bibtex:
@article{he2018brain,
title={Brain MRI Image Super Resolution using Phase Stretch Transform and Transfer Learning},
author={He, Sifeng and Jalali, Bahram},
journal={arXiv preprint arXiv:1807.11643},
year={2018}
}
Special thanks to Dr. Cejo Konuparamban Lonappan for his support and guidance.
The code is released under GNU General Public License 3.0. Please refer to the LICENSE file for details.