Awesome

Swig-Python VNLB

A Swig-Python Wrapper for Video Non-Local Bayesian Denoising (C++ code originally from Pablo Arias)

Install

$ git clone https://github.com/gauenk/svnlb/
$ cd svnlb
$ python -m pip install -r requirements.txt --user
$ ./install.sh

Usage

We expect the images to be shaped (nframes,channels,height,width) with pixel values in range [0,...,255.]. The color channels are ordered RGB. Common examples of noise levels are 10, 20 and 50. See scripts/example.py for more details.

import svnlb
import numpy as np

# -- use enough threads --
svnlb.check_omp_num_threads()

# -- get data --
clean = 255.*np.random.rand(5,3,64,64)
# (nframes,channels,height,width)

# -- add noise --
std = 20.
noisy = np.random.normal(clean,scale=std)

# -- TV-L1 Optical Flow --
fflow,bflow = svnlb.swig.runPyFlow(noisy,std)

# -- Video Non-Local Bayes --
result = svnlb.swig.runPyVnlb(noisy,std,{'fflow':fflow,'bflow':bflow})
denoised = result['denoised']

# -- compute denoising quality --
psnrs = svnlb.utils.compute_psnrs(clean,denoised)
print("PSNRs:")
print(psnrs)

Comparing with C++ Code

The outputs from the Python Wrapper and the C++ Code are exactly equal. To demonstrate this claim, we provide the scripts/compare_cpp.py script. We have two examples of the C++ Code output ready for download using the respective scripts/download_davis*.sh files. To run the data downloading scripts, type:

$ ./scripts/download_davis_64x64.sh

To run the comparison, type:

$ export OMP_NUM_THREADS=4
$ 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 Wrapper and C++ Code.

	noisyForFlow	noisyForVnlb	fflow	bflow	basic	denoised
Total Error (cv2)	0.000505755	0	504.308	21.643	0	0
Total Error (cpp)	0	0	0	0	0	0

Details can be found in docs/COMPARE.md

Dependencies

The code depends on the following packages:

CBLAS, LAPACKE: operations with matrices
OpenMP: parallelization [optional, but recommended]
libpng, libtiff and libjpeg: image i/o

NOTE: By default, the code is compiled with OpenMP multithreaded parallelization enabled (if your system supports it). Use the OMP_NUM_THREADS enviroment variable to control the number of threads used.

Credits

This code provides is a Python wrapper over an implementation of the video denoising method (VNLB-H) described in:

P. Arias, J.-M. Morel. "Video denoising via empirical Bayesian estimation of space-time patches", Journal of Mathematical Imaging and Vision, 60(1), January 2018.

Please cite the publication if you use results obtained with this code in your research.

Original Code linked here
C++ (and Primary) Author: Pablo Arias pariasm@gmail.com
For computing the optical flow, it includes the IPOL implementation of the TV-L1 optical flow method of Zack and Pock and Bischof.
For image I/O, we use Enric Meinhardt's iio.
For SWIG-Python, Kent Gauen wrote this wrapper kent.gauen@gmail.com

LICENSE

Licensed under the GNU Affero General Public License v3.0, see LICENSE.