https://github.com/schen59/SRLab

single image super resolution algorithm implementation
https://github.com/schen59/SRLab

Last synced: 3 months ago
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single image super resolution algorithm implementation

• Host: GitHub
• URL: https://github.com/schen59/SRLab
• Owner: schen59
• Created: 2014-02-23T08:43:47.000Z (over 10 years ago)
• Default Branch: master
• Last Pushed: 2016-06-20T04:24:58.000Z (over 8 years ago)
• Last Synced: 2024-05-27T12:12:09.034Z (6 months ago)
• Language: Python
• Size: 1.17 MB
• Stars: 36
• Watchers: 9
• Forks: 16
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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README

        
# SRLab

Single image super resolution algorithm implementation (in progress)

## Demo:

### Source(128X128)

![Source(128X128):](test_data/babyface_4.png)

### 4X

![4X](test_data/babyface_4x.png)

Project Home Page [here](https://schen59.github.io/#/SRLab)

More Project [Demo](http://shaofeng_2010.gegahost.net/SR/SingleImgSR.html)

## Requirement:

* Supported python version 2.7

* pip install Pillow

* pip install mock==1.0.1

* pip install six

* pip install -U numpy scipy scikit-learn

## Example:

    image = Image.open("test_data/babyface_4.png")

    sr_image = SRImageFactory.create_sr_image(image)

    reconstructed_sr_image = sr_image.reconstruct(2, 'iccv09')

    reconstructed_sr_image.save("test_data/babyface_sr.png", "png")

## Note:

Need to work more on performance, it will take around 20 seconds to reconstruct a 128*128 image to

2X its original size, and take around 1 minutes to 4X its original size.(On i7 Cpu, 8G ram)

### Run the examples in example/sr_image_example.py:

Add root directory of SRLab to PYTHONPATH:

    export PYTHONPATH=$PYTHONPATH:~/schen59/SRLab

Go to the example directory:

    cd example

Run the example script:

    python sr_image_example.py