https://github.com/gaochen315/panoramicrpca
Panoramic robust PCA for foreground-background separation on noisy, free-motion camera video
https://github.com/gaochen315/panoramicrpca
denoising foreground-background
Last synced: 7 months ago
JSON representation
Panoramic robust PCA for foreground-background separation on noisy, free-motion camera video
- Host: GitHub
- URL: https://github.com/gaochen315/panoramicrpca
- Owner: gaochen315
- License: mit
- Created: 2018-10-06T20:11:40.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-10-31T20:59:45.000Z (over 5 years ago)
- Last Synced: 2023-10-22T21:27:34.997Z (over 1 year ago)
- Topics: denoising, foreground-background
- Language: MATLAB
- Homepage:
- Size: 35.4 MB
- Stars: 48
- Watchers: 3
- Forks: 9
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Panoramic Robust PCA
This package contains the implementation and example usages of the
Panoramic Robust Principal Component Analysis (PRPCA) method, an algorithm
for producing robust estimates of the foreground and background of a scene
from possibly corrupted video with arbitrary camera motion. For more
information, see the [project website](https://gaochen315.github.io/pRPCA).
If you have any questions, contact
[Chen Gao](mailto:[email protected]?subject=PRPCA%20code).
## Quickstart
To generate some reconstructions using PRPCA, simply run the examples in
`demo.m`. The script contains examples of processing both grayscale and
color videos in the noiseless and noisy (salt and pepper) regimes.
## Contents
#### Data
- `data/tennis`: 35 frames of the Tennis sequence. Color images with
resolution 480 x 854. Also contains homographies computed by the
`registerVideo` function for mapping each frame to the coordinates of
the 17th frame of the sequence
#### Scripts
- `demo.m`: Example uses of PRPCA to perform robust foreground-background
separation from video with arbitrary camera motion
#### Main Functions
- `src/PRPCA.m`: Main function for the PRCPA method as described in the papers
cited below
- `src/PRPCA_noiseless.m`: An implementation of PRPCA intended for the
noiseless (possibly moving camera) setting. This implementation uses
ell-1 based regularization for the foreground and omits the total
variation-regularized component, which is not essential when there is
no corruption to disentangle from the foreground component
#### Other Functions
- `src/adjustLS.m`: Adjusts the low-rank and sparse components of a
total variation-regularized foreground-background decomposition to
account for the constant-offset-invariance of the TV penalty
- `src/augRobustPCA.m`: Solves the Augmented Robust PCA problem with low-rank
background, total variation-regularized foreground, and an addition
sparse component to capture residual corruptions
- `src/cleanBackground.m`: Cleans the background component of a panoramic
foreground-background grayscale video reconstruction
- `src/cleanBackground_RGB.m`: Cleans the background component of a panoramic
foreground-background RGB video reconstruction
- `src/correctLighting.m`: Attempts to correct non-uniform lighting effects in
a panoramic image
- `src/formatForDisplay.m`: Formats a decomposition for display by
appropriately scaling and clamping the components to [0, 1]
- `src/OptShrink.m`: Implementation of the data-driven OptShrink low-rank
matrix estimator
- `src/pano2video.m`: Transforms a panoramic grayscale video back to its
native perspective
- `src/pano2video_RGB.m`: Transforms a panoramic RGB video back to its native
perspective
- `src/PlayMovie.m`: Utility function for visualizing a data tensor as a video
- `src/registerVideo.m`: registers the frames of a video to a common reference
perspective
- `src/robustPCA.m`: Approximately solves the Robust PCA problem using an
OptShrink-based low-rank update
- `src/tvdn.m`: Solves the subsampled total-variation with denoising (TVDN)
problem
- `src/writeGIF.m`: Utility function for writing a GIF image.
## License
This code is made available with the MIT license. See `LICENSE` for
details.
## References
If you use this code, we request that you cite the following papers:
```
@article{moore2017panoramic,
author = {Moore, Brian E* and Gao, Chen* and Nadakuditi, Raj Rao},
title = {Panoramic Robust PCA for Foreground-Background Separation on Noisy, Free-Motion Camera Video},
journal = {IEEE Transactions on Computational Imaging},
volume = {5},
number = {2},
pages = {195--211},
year = {2019},
}
@inproceedings{gao2017augmented,
author = {Gao, Chen and Moore, Brian E and Nadakuditi, Raj Rao},
title = {Augmented robust PCA for foreground-background separation on noisy, moving camera video},
booktitle = {2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)}
month = {November},
year = {2017},
pages = {1240-1244}
}
```