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Image Denoising Codes using STROLLR learning, the Matlab implementation of the paper in ICASSP2017
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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], [PDF available], [Code]

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 and Transform Learning Site.

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.

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