imageProcessing | Survey of Image Denoising Techniques

Howdy! If you're reading this it means that you've stumbled across a bit of code that I've written as a class project.

The code in this repo is designed to perform a variety of image denoising techniques in CUDA. This code is intended to add noise to clean base images, and then remove it using some of the included methods--however, it can be used on images without adding noise by setting the paramater to 0.

The code should be built using cmake, but a manual makefile is also included for those on Vocareum.

The primary built target is called imageProcessing, and it contains a few methods that

• add additive white gaussian noise
• add salt and pepper noise
• do a median filter
• do svd on an image
• do a fourier low pass filter
• do variational diffusion
• do total variation minimization
• and more!

Python scripts are included to extract(pixelExtractor) images into a .pxa (pixel array) format, and compress(pixelCompressor) .pxa files back into .png or other image formats.

Good luck. Hopefully this code is ~helpful~ useful to you.

Building the code

cmake version 3.21 or later is required to build the code for this project. If you are in the Vocareum enviroment, a MANUALMakefile has been provided, and should be used. To install the required Python 3 packages, and compile the main code with cmake run the following:

cd /.../.../imageProcessing/
pip install -r requirements.txt # may need to be pip3 
mkdir build
cd build
cmake ..
make -j

Ta da! Hopefully everything built correctly.

If you are on Vocareum, running a simple:

cd /.../.../imageProcessing/
pip install -r requirements.txt # may need to be pip3 (NOTE: vocareum should already have the python packages installed, should be okay if this fails)
mv MANUALMakefile Makefile
make -j

Should do the trick.

Demo usage and examples

I recommend using only smaller images (up to 400x400), as it can be tricky to see the effects with lots and lots of pixels.

If you do not have a testing image--images/camera.png, images/butterfly.png, images/moon.png, or images/barn.png--can be used by copying them to your working/build directory.

If you need to convert an image to .pxa format, you can run python3 pixelExtractor.py inputfilename.png to get a inputfilename.pxa file. Note: Most file formats are supported via the Python Image Library (PIL), so you can use .jpeg, .png, etc.

To just add additive white gaussian noise w/ std. deviation 20 ./imageProcessing inputfilename.pxa outputfilename.out 1 0 20

To just add salt and pepper noise w/ rate 30 ./imageProcessing inputfilename.pxa outputfilename.out 2 0 30

To perform total variational reduction on an image with additive white gaussian noise w/ std. deviation 20 using lambda .1 ./imageProcessing inputfilename.pxa outputfilename.out 1 6 20 .1

To perform a median filter on an image with salt and pepper noise w/ rate 30 using lambda 1 ./imageProcessing inputfilename.pxa outputfilename.out 2 4 30 1

To view the processed images you can use python3 pixelCompressor.py outputfilename.out outputfilename.png to get a finished image.