This code is a Python API for Video Non-Local Bayesian Denoising (VNLB). The original C++ code is from Pablo Arias. For easier interfacing, a Swig-Python Wrapper of the original C++ Code is available here.
The outputs from this VNLB code and the C++ Code are almost equal. The primary difference between to two method is the way in which we achieve parallelism. This difference impacts the final PSNR, especially on smaller images. The C++ code uses OPENMP to execute parallel CPU threads and chunks the image into sections. In this code base, a batch of random pixels are selected to searched and this searching is executed in parallel on a GPU. A secondary (and seemingly less important) difference between the two methods is how the precision of the inherent different of matrix multiplication between the C++ LAPLACK package the Python Pytorch package. However, since the image denoising community is interested in differences of approximately 1e-3 (and smaller), these small errors can potentially meaningfully change the final denoised PSNR.
To demonstrate the difference between the two methods, we provide the compare_cpp.py script. We have two examples from the C++ Code provided in the data/ folder. For reproducibility, details to re-create the C++ Code results are included in the docs/VNLB.md file. To run the comparison, run the following:
$ python scripts/compare_cpp.py
The script prints the below table. Each element of the table is the sum of the absolute relative error between the outputs from the Python API and C++ Code.
| basic | denoised | |
|---|---|---|
| Ave Rel. Error (basic) | 0.023508 | nan |
| cpp_psnr | 31.4275 | 31.6715 |
| py_psnr | 31.4137 | 31.6513 |
| Abs. Error (PSNR) | 0.0138222 | 0.0201801 |
| Rel. Error (PSNR) | 0.000439814 | 0.000637169 |
| Ave Rel. Error (denoised) | nan | 0.0213285 |
On small images, the CPU is x10 faster than the GPU code. On larger images, the two methods are approximately equal... The Python code takes about 3 seconds longer than the C++ Code to execute.
First, export a short-cut to the directory
$ export PYVNLB_HOME="/pick/your/path/"
such that ls $PYTHON_HOME/ shows "data/" with "data/davis_baseball" as a subdirectory.
To time the C++ algorithms, please install the original C++ code. Then run the following command in the "scripts" directory,
$ time `./vnlb-gt.sh $PYVNLB_HOME/data/davis_baseball/%05d.jpg 0 4 20 $PYVNLB_HOME/data/davis_baseball_64x64/vnlb/ "-px1 7 -pt1 2 -px2 7 -pt2 2 -verbose"`
#davis
real 2m25.276s
user 12m25.894s
sys 3m5.471s
$ cd vnlb/
$ rm -r ./__pycache__
$ time `python ./scripts/example.py`
#davis
real 2m28.202s
user 12m18.676s
sys 3m10.941s
On the davis example, the original execution time of the C++ Code and Python API is X and Y, respectively. ...