<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In this paper, we propose a Detail-Preserving Sparse Model (DPSM) for de-noising of images that are usually interfered by noise on the Wireless Multimedia Sensor Network (WMSN). Specifically, based on the Structural SIMilarity (SSIM), the DPSM first incorporates a structural-preserving constraint, which enables the structure in the reconstructed image to be close to the ideal no-noise image. In addition, the DPSM adopts a residual ratio as the stopping condition of the sparse solution algorithm (e.g., Orthogonal Matching Pursuit), which enables the structures to be reconstructed under high noise conditions. The experimental results on several WMSN images have demonstrated the superiority of the proposed DPSM method over several well-known de-noising approaches in terms of PSNR and SSIM.</p></abstract>

In this paper, we propose a Detail-Preserving Sparse Model (DPSM) for de-noising of images that are usually interfered by noise on the Wireless Multimedia Sensor Network (WMSN). Specifically, based on the Structural SIMilarity (SSIM), the DPSM first incorporates a structural-preserving constraint, which enables the structure in the reconstructed image to be close to the ideal no-noise image. In addition, the DPSM adopts a residual ratio as the stopping condition of the sparse solution algorithm (e.g., Orthogonal Matching Pursuit), which enables the structures to be reconstructed under high noise conditions. The experimental results on several WMSN images have demonstrated the superiority of the proposed DPSM method over several well-known de-noising approaches in terms of PSNR and SSIM.

Wireless Multimedia Sensor Network Image De-Noising via a Detail-Preserving Sparse Model

School of Communication and Electronic Engineering, Hunan City University, Yiyang, China

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

In this paper, we propose a Detail-Preserving Sparse Model (DPSM) for de-noising of images that are usually interfered by noise on the Wireless Multimedia...

Wireless Multimedia Sensor Network Image De-Noising via a Detail-Preserving Sparse Model

Online veröffentlicht: 30. Dez. 2015

Seitenbereich: 57 - 69

DOI: https://doi.org/10.1515/cait-2015-0067

Schlüsselwörter
Structural similarity, residual ratio, wireless multimedia sensor network, sparse representation, de-noising

© 2015 Zhi Cui et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Wireless Multimedia Sensor Network Image De-Noising via a Detail-Preserving Sparse Model

Online veröffentlicht: 30. Dez. 2015

Seitenbereich: 57 - 69

DOI: https://doi.org/10.1515/cait-2015-0067

SchlüsselwörterStructural similarity, residual ratio, wireless multimedia sensor network, sparse representation, de-noising

© 2015 Zhi Cui et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Structural similarity, residual ratio, wireless multimedia sensor network, sparse representation, de-noising