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  • 标题:Block Compressed Sensing of Self-adaptive Measurement and Combinatorial Optimization
  • 本地全文:下载
  • 作者:Li Mingxing ; Chen Xiuxin ; Su Weijun
  • 期刊名称:International Journal of Hybrid Information Technology
  • 印刷版ISSN:1738-9968
  • 出版年度:2015
  • 卷号:8
  • 期号:5
  • 页码:227-236
  • DOI:10.14257/ijhit.2015.8.5.25
  • 出版社:SERSC
  • 摘要:The block compressed sensing has brought forth the problem that the reconstructed image is of lower quality compared with that of the compressed sensing. A new method is proposed in this paper, named as Block Compressed Sensing of Self-adaptive Measurement and Combinatorial Optimization, which capably solves the problem. According to different sparsity of each image block, we firstly measure the blocks by using different projections; then, we choose measurement with the optimal reconstruction as the final measurement. Eventually, reconstruct the original image using the optimal measurement we got. The proposed method outperforms the compressed sensing in terms of real-time and better reconstruction quality is achieved than the block compressed sensing. Our experimental results verify the superiority of the proposed method.
  • 关键词:Block Compressed Sensing; Sparsity; Self-adaptive Measurement; ; Combinatorial Optimization; Reconstruction
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