Image denoising via group sparse eigenvectors of Graph Laplacian. Tang, Y., Chen, Y., Xu, N., Jiang, A., & Zhou, L. In 2016 24th European Signal Processing Conference (EUSIPCO), pages 2171-2175, Aug, 2016.
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Paper doi abstract bibtex
Paper doi abstract bibtex In this paper, a group sparse model using Eigenvectors of the Graph Laplacian (EGL) is proposed for image denoising. Unlike the heuristic setting for each image and for each noise deviation in the traditional denoising method via the EGL, in our group-sparse-based method, the used eigenvectors are adaptively selected with the error control. Sequentially, a modified group orthogonal matching pursuit algorithm is developed to efficiently solve the optimal problem in this group sparse model. The experiments show that our method can achieve a better performance than some well-developed denoising methods, especially in the noise of large deviations and in the SSIM measure.
@InProceedings{7760633,
author = {Y. Tang and Y. Chen and N. Xu and A. Jiang and L. Zhou},
booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)},
title = {Image denoising via group sparse eigenvectors of Graph Laplacian},
year = {2016},
pages = {2171-2175},
abstract = {In this paper, a group sparse model using Eigenvectors of the Graph Laplacian (EGL) is proposed for image denoising. Unlike the heuristic setting for each image and for each noise deviation in the traditional denoising method via the EGL, in our group-sparse-based method, the used eigenvectors are adaptively selected with the error control. Sequentially, a modified group orthogonal matching pursuit algorithm is developed to efficiently solve the optimal problem in this group sparse model. The experiments show that our method can achieve a better performance than some well-developed denoising methods, especially in the noise of large deviations and in the SSIM measure.},
keywords = {image denoising;iterative methods;time-frequency analysis;image denoising;group sparse Eigenvectors;eigenvectors of the graph Laplacian;heuristic setting;noise deviation;denoising method;error control;modified group orthogonal matching pursuit algorithm;group sparse model;denoising methods;Sparse matrices;Noise reduction;Noise measurement;Laplace equations;Image denoising;Matching pursuit algorithms;Signal processing algorithms},
doi = {10.1109/EUSIPCO.2016.7760633},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2016/papers/1570255675.pdf},
}Downloads: 0
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