https://github.com/wenbihan/strollr2d_icassp2017
Image Denoising Codes using STROLLR learning, the Matlab implementation of the paper in ICASSP2017
https://github.com/wenbihan/strollr2d_icassp2017
image-denoising joint-models lowrankdenoising self-similarity sparsity state-of-the-art transform-learning unsupervised-learning
Last synced: 8 months ago
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Image Denoising Codes using STROLLR learning, the Matlab implementation of the paper in ICASSP2017
- Host: GitHub
- URL: https://github.com/wenbihan/strollr2d_icassp2017
- Owner: wenbihan
- Created: 2018-01-13T22:16:19.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2018-01-13T23:00:56.000Z (almost 8 years ago)
- Last Synced: 2025-01-07T18:18:39.109Z (9 months ago)
- Topics: image-denoising, joint-models, lowrankdenoising, self-similarity, sparsity, state-of-the-art, transform-learning, unsupervised-learning
- Language: Matlab
- Size: 2.08 MB
- Stars: 26
- Watchers: 3
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# strollr2d_icassp2017
Image Denoising Codes using STROLLR learning, the Matlab implementation of the paper in ICASSP2017
STROLLR2D image denoising accompanies the following publication:
"When Sparsity Meets Low-Rankness: Transform Learning With Non-Local Low-Rank Constraint for Image Restoration", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017. [[ICASSP 2017](http://ieeexplore.ieee.org/abstract/document/7952566/)], [[PDF available](http://transformlearning.csl.illinois.edu/assets/Bihan/ConferencePapers/BihanICASSP2017strollr.pdf)], [[Code](https://github.com/wenbihan/strollr2d_icassp2017)]
Description:
-----
STROLLR is an image denoising framework based on a joint adaptive patch sparse and group low-rank model learning scheme (STROLLR). The proposed scheme is capable of better representing natural images by exploiting both its local sparsity and non-local similarity. Our numerical experiments show promising performance for the proposed image denoising method compared to popular prior or state-of-the-art methods.
You can download our other software packages at: [My Homepage](http://web.engr.illinois.edu/~bwen3/) and [Transform Learning Site](http://transformlearning.csl.illinois.edu/).
Paper
In case of use, please cite our publications:
B. Wen, Y. Li, and Y. Bresler, “When Sparsity Meets Low-Rankness: Transform Learning With Non-Local Low-Rank Constraint for Image Restoration,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017.
```
@inproceedings{wen2017strollr2d,
title = {When sparsity meets low-rankness: Transform learning with non-local low-rank constraint for image restoration},
author = {Wen, Bihan and Li, Yanjun and Bresler, Yoram},
booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {2297--2301},
year = {2017},
organization={IEEE}
}
```
Use
---
All codes are subject to copyright and may only be used for non-commercial research. In case of use, please cite our publication.
Contact Bihan Wen (bihan.wen.uiuc@gmail.com) for any questions.
Acknowledgement
---
The development of this software was supported in part by the National Science Foundation (NSF) under grants CCF 06-35234 and CCF 10-18660.