@InProceedings{6952591,
  author = {M. Joneidi and M. Sadeghi and M. Sahraee-Ardakan and M. Babaie-Zadeh and C. Jutten},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {A study on clustering-based image denoising: From global clustering to local grouping},
  year = {2014},
  pages = {1657-1661},
  abstract = {This paper studies denoising of images contaminated with additive white Gaussian noise (AWGN). In recent years, clustering-based methods have shown promising performances. In this paper we show that low-rank subspace clustering provides a suitable clustering problem that minimizes the lower bound on the MSE of the denoising, which is optimum for Gaussian noise. Solving the corresponding clustering problem is not easy. We study some global and local sub-optimal solutions already presented in the literature and show that those that solve a better approximation of our problem result in better performances. A simple image denoising method based on dictionary learning using the idea of gain-shaped K-means is also proposed as another global suboptimal solution for clustering.},
  keywords = {AWGN;image denoising;mean square error methods;pattern clustering;clustering-based image denoising method;global clustering;local grouping;additive white Gaussian noise;AWGN;low-rank subspace clustering;lower bound;local sub-optimal solutions;dictionary learning;global suboptimal solution;gain-shaped K-means;mean square error;Dictionaries;Image denoising;Clustering algorithms;Noise reduction;AWGN;Noise measurement;Eigenvalues and eigenfunctions;Image denoising;data clustering;dictionary learning;sparse representation},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569925433.pdf},
}

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In recent years, clustering-based methods have shown promising performances. In this paper we show that low-rank subspace clustering provides a suitable clustering problem that minimizes the lower bound on the MSE of the denoising, which is optimum for Gaussian noise. Solving the corresponding clustering problem is not easy. We study some global and local sub-optimal solutions already presented in the literature and show that those that solve a better approximation of our problem result in better performances. 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Jutten},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {A study on clustering-based image denoising: From global clustering to local grouping},\n year = {2014},\n pages = {1657-1661},\n abstract = {This paper studies denoising of images contaminated with additive white Gaussian noise (AWGN). In recent years, clustering-based methods have shown promising performances. In this paper we show that low-rank subspace clustering provides a suitable clustering problem that minimizes the lower bound on the MSE of the denoising, which is optimum for Gaussian noise. Solving the corresponding clustering problem is not easy. We study some global and local sub-optimal solutions already presented in the literature and show that those that solve a better approximation of our problem result in better performances. 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