Reconstruction algorithm for block compressed sensing based on variation model
The algorithms for block compressed sensing based on total variation and mixed variation (abbreviated as BCS-TV and BCS-MV) models were proposed to improve the performance of current reconstruction algorithms for the block-based compressed sensing. In the measuring phase, an image was sampled block-...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2016-01-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2016011/ |
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| Summary: | The algorithms for block compressed sensing based on total variation and mixed variation (abbreviated as BCS-TV and BCS-MV) models were proposed to improve the performance of current reconstruction algorithms for the block-based compressed sensing. In the measuring phase, an image was sampled block-by-block. In the recovering period, it took the sparse regularization of the natural image as a priori knowledge, and approached the target function within the whole image through the modified augmented Lagrange method and alternating direction method of multipliers (ALM-ADMM). The method proposed achieves average PSNR gain of 1.5 dB and SSIM gain of 0.05 at a more stable running speed, over the previous uniformly block-based compressed sensing. It is particularly suitable for the applications of the multimedia data processing with fixed transmission delay. |
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| ISSN: | 1000-436X |