caa a22 4500 606205594 CHVBK 20210128101008.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s00034-014-9882-6 doi (NATIONALLICENCE)springer-10.1007/s00034-014-9882-6 Clustering-Based Image Sparse Denoising in Wireless Multimedia Sensor Networks [Elektronische Daten] [Hui Luo, Hongliang Chu, Yao Xu] With the increasing interest in the deployment of wireless multimedia sensor networks (WMSN), new challenges have arisen with the complexity and high noise level of the monitoring environment. Given that the noise severely impairs the quality and visibility of video images perceived by sensors, video image denoising naturally becomes the key to ensure the validity and reliability of the WMSN video monitoring. In this paper, the sparse denoising algorithm via clustering-based sparse representation is proposed. Firstly, WMSN images are, respectively, clustered based on the pixel intensity of regions of interest (ROIs), which are determined in terms of Bayesian theorem. Secondly, in the light of nonlocal self-similarity regularizer provided by the ROI-based WMSN images clustering, clustering-based sparse representation builds a new sparse denoising model exploiting both sparsity and nonlocal self-similarity to improve the quality of reconstructed images. At last, a surrogate-function-based iterative shrinkage solution has been developed to solve the double-header $$l_{1}$$ l 1 -optimization problem. Experimental results showed that the performance of the approach to image denoising is competitive, qualitative, as well as quantitative, and suitable for the WMSN video image denoising. Springer Science+Business Media New York, 2014 Structure clustering nationallicence Sparse representation nationallicence WMSN nationallicence Video image denoising nationallicence Luo Hui School of Information Engineering, East China Jiaotong University, 330013, Nanchang, China aut Chu Hongliang School of Information Engineering, East China Jiaotong University, 330013, Nanchang, China aut Xu Yao School of Information Engineering, East China Jiaotong University, 330013, Nanchang, China aut Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/3(2015-03-01), 1027-1040 0278-081X 34:3<1027 2015 34 34 https://doi.org/10.1007/s00034-014-9882-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00034-014-9882-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Luo Hui School of Information Engineering, East China Jiaotong University, 330013, Nanchang, China aut NATIONALLICENCE 700 1- Chu Hongliang School of Information Engineering, East China Jiaotong University, 330013, Nanchang, China aut NATIONALLICENCE 700 1- Xu Yao School of Information Engineering, East China Jiaotong University, 330013, Nanchang, China aut NATIONALLICENCE 773 0- Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/3(2015-03-01), 1027-1040 0278-081X 34:3<1027 2015 34 34