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A Python implementation of RAISR
| Date | Stars |
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| 2026-07-24 | 561 |
| 2026-07-25 | 561 |
| 2026-07-28 | 561 |
| 2026-07-30 | 561 |
| 2026-08-06 | 561 |
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# RAISR A Python implementation of [RAISR](http://ieeexplore.ieee.org/document/7744595/) ## How To Use ### Prerequisites You can install most of the following packages using [pip](https://pypi.python.org/pypi/pip). * [OpenCV-Python](https://pypi.python.org/pypi/opencv-python) * [NumPy](http://www.numpy.org/) * [SciPy](https://www.scipy.org/) * [Python Imaging Library (PIL)](http://www.pythonware.com/products/pil/) * [Matplotlib](https://matplotlib.org/) * [scikit-image](http://scikit-image.org/) ### Training Put your training images in the `train` directory. The training images are the **high resolution (HR)** ones. Run the following command to start training. ``` python train.py ``` In the training stage, the program virtually downscales the high resolution images. The program then trains the model using the downscaled version images and the original HR images. The learned filters `filter.p` will be saved in the root directory of the project. The result Q, V matrix (`q.p` and `v.p`) will also be saved for further retraining. To train an improved model with your previous Q, V, use the following command. ``` python train.py -q q.p -v v.p ``` ### Testing Put your testing images in the `test` directory. Basically, you can use some **low resolution (LR)** images as your testing images. By running the following command, the program takes `filter.p` generated by training as your default filters. ``` python test.py ``` The result (HR version of the testing images) will be saved in the `results` directory. To use an alternative filter file, take using the pretrained `filters/filter_BSDS500` for example, use the following command. ``` python test.py -f filters/filter_BSDS500 ``` ## Visualization Visualing the learned filters ``` python train.py -p ``` Visualing the process of RAISR image upscaling ``` python test.py -p ``` For more details, use the help command argument `-h`. ## Testing Results Comparing between original image, bilinear interpolation and RAISR: | Origin | Bilinear Interpolation | RAISR | |:----------------------:|:----------------------:|:----------------------:| |||| Other results using images taken from [BSDS500 database](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html) and [ArTe-Lab 1D Medium Barcode Dataset](http://artelab.dista.uninsubria.it/downloads/datasets/barcode/medium_barcode_1d/medium_barcode_1d.html): | Origin | RAISR | |:-------------:|:-------------:| ||| ||| ||| ## Contribute We actively welcome pull requests. Learn how to [contribute](https://github.com/movehand/raisr/blob/master/docs/CONTRIBUTING.md). ## 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
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0ebfbe721f4a5360, topic:super-resolution, readme:upscaling, readme:super resolution