This is a Fast Bilateral Filter Approximation inspired by an academic paper.
Hopefully I will:
- Rewrite this implementation using C/C++ for computatively intensive parts.
- Refactor utility scripts for better performance.
- Replace the test runner to be more general purpose with a better interface.
I plan to implement above improvements until end of 2021 (Edit: postponed due to busyness).
This code highly inspired by:
[1] https://gist.github.com/ginrou/02e945562607fad170a1#file-fastbilateral-hpp
[2] https://gist.github.com/jackdoerner/b81ad881c4064470d3c0#file-bilateral_approximation-py
[3] https://github.com/KirillLykov/cvision-algorithms/blob/master/src/fastBilateralFilter.m
[4] http:https://people.csail.mit.edu/jiawen/software/bilateralFilter.m
Information of functions provided in script functions. If you want to use a standartized version use:
python3 runner.py
and type filenames with locations. Type '!-end-!' to end giving inputs. At least 2 images are needed.
This method adds Random Gaussian Noise to images and processed with Bilateral Filter Implementations.
To reduce resolution of images before processing:
python3 lowerres.py
Inputs work exactly as runner.py.
Refer to The Report, The Fast Approximation. For more information a paper is provided in The Report.
- There is no strict memory managment in 'runner.py' as it is just a testing script.
- The picked configurations are explaned in The Report.
- For more information contact one of the emails provided in 'fastbilateralapprox.py'.