simulated-annealing-denoising

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##Dependencies

1. pillow
2. Numpy
3. matplotlib

The source code can be run under windows or linux with python 2.7+ and the libraries above.

##Directory structure

./
├── doc
│   └── report.pdf  (the report)
├── img
│   ├── best.png  (SA-denoised result)
│   ├── flipped.png  (flipped image)
│   ├── ICM-energy-time.png  (time-energy plot of ICM)
│   ├── icm.png  (ICM-denoised result)
│   ├── in.png  (original image)
│   └── SA-energy-time.png  (time-energy plot of SA)
└── src
    ├── binarize.py  (script to convert the input to a binary image)
    ├── count.py  (script to evaluate the denoised results)
    ├── denoise.py  (script to denoise the results with either SA or ICM)
    ├── flip.py  (script to flip the image)
    └── util.py  (utilities. Configurable arguments are defined here.)

##How to generate the results

Note: python scripts should be run under the src directory. All images will be placed under the img directory.

1. Place the original image called in.png under img directory.
2. Enter the src directory, run python binarize.py. It will convert the in.png to a binary image and overwrite it.
3. Run python flip.py, which will generate the flipped image named flipped.png.
4. Run python denoise.py to denoise the flipped image using simulated annealing. The result will be named best.png. Temporary results (temp-*.png) and the time-energy plot (SA-energy-time.png) will be saved, too.
5. Run python denoise.py -m "ICM" -o "icm.png" to denoise the flipped image using ICM. The result will be named icm.png. Temporary results (icm-temp-*.png) and the time-energy plot (ICM-energy-time.png) will be saved, too.
6. Run python count.py to see how many pixels of the output of SA agree to the original image. To do the same evaluation for ICM, run python count.py -o "icm.png"

You can run python denoise.py -h to see what arguments are configurable.

##About

• Github repository
• Time: Nov. 2014