Compressed Sensing Based Apple Image Measurement Matrix Selection
The purpose of this paper is to design a measurement matrix of apple image based on compressed sensing to realize low cost sampling apple image. Compressed sensing based apple image sampling method makes a breakthrough to the limitation of the Nyquist sampling theorem. By investigating the matrix me...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-07-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/901073 |
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| _version_ | 1832555296577290240 |
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| author | Ying Xiao Wanlin Gao Ganghong Zhang Han Zhang |
| author_facet | Ying Xiao Wanlin Gao Ganghong Zhang Han Zhang |
| author_sort | Ying Xiao |
| collection | DOAJ |
| description | The purpose of this paper is to design a measurement matrix of apple image based on compressed sensing to realize low cost sampling apple image. Compressed sensing based apple image sampling method makes a breakthrough to the limitation of the Nyquist sampling theorem. By investigating the matrix measurement signal, the method can project a higher dimensional signal to a low-dimensional space for data compression and reconstruct the original image using less observed values. But this method requires that the measurement matrix and sparse transformation base satisfy the conditions of RIP or incoherence. Real time acquiring and transmitting apple image has great importance for monitoring the growth of fruit trees and efficiently picking apple. This paper firstly chooses sym5 wavelet base as apple image sparse transformation base, and then it uses Gaussian random matrices, Bernoulli random matrices, Partial Orthogonal random matrices, Partial Hadamard matrices, and Toeplitz matrices to measure apple images, respectively. Using the same measure quantity, we select the matrix that has best reconstruction effect as the apple image measurement matrix. The reconstruction PSNR values and runtime were used to compare and contrast the simulation results. According to the experiment results, this paper selects Partial Orthogonal random matrices as apple image measurement matrix. |
| format | Article |
| id | doaj-art-f0eeae8f26b7491896a9ec9386edc4a1 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-f0eeae8f26b7491896a9ec9386edc4a12025-02-03T05:48:37ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-07-011110.1155/2015/901073901073Compressed Sensing Based Apple Image Measurement Matrix SelectionYing Xiao0Wanlin Gao1Ganghong Zhang2Han Zhang3 College of Information and Electrical Engineering, China Agriculture University, Beijing 100083, China College of Information and Electrical Engineering, China Agriculture University, Beijing 100083, China College of Information and Electrical Engineering, China Agriculture University, Beijing 100083, China Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, ChinaThe purpose of this paper is to design a measurement matrix of apple image based on compressed sensing to realize low cost sampling apple image. Compressed sensing based apple image sampling method makes a breakthrough to the limitation of the Nyquist sampling theorem. By investigating the matrix measurement signal, the method can project a higher dimensional signal to a low-dimensional space for data compression and reconstruct the original image using less observed values. But this method requires that the measurement matrix and sparse transformation base satisfy the conditions of RIP or incoherence. Real time acquiring and transmitting apple image has great importance for monitoring the growth of fruit trees and efficiently picking apple. This paper firstly chooses sym5 wavelet base as apple image sparse transformation base, and then it uses Gaussian random matrices, Bernoulli random matrices, Partial Orthogonal random matrices, Partial Hadamard matrices, and Toeplitz matrices to measure apple images, respectively. Using the same measure quantity, we select the matrix that has best reconstruction effect as the apple image measurement matrix. The reconstruction PSNR values and runtime were used to compare and contrast the simulation results. According to the experiment results, this paper selects Partial Orthogonal random matrices as apple image measurement matrix.https://doi.org/10.1155/2015/901073 |
| spellingShingle | Ying Xiao Wanlin Gao Ganghong Zhang Han Zhang Compressed Sensing Based Apple Image Measurement Matrix Selection International Journal of Distributed Sensor Networks |
| title | Compressed Sensing Based Apple Image Measurement Matrix Selection |
| title_full | Compressed Sensing Based Apple Image Measurement Matrix Selection |
| title_fullStr | Compressed Sensing Based Apple Image Measurement Matrix Selection |
| title_full_unstemmed | Compressed Sensing Based Apple Image Measurement Matrix Selection |
| title_short | Compressed Sensing Based Apple Image Measurement Matrix Selection |
| title_sort | compressed sensing based apple image measurement matrix selection |
| url | https://doi.org/10.1155/2015/901073 |
| work_keys_str_mv | AT yingxiao compressedsensingbasedappleimagemeasurementmatrixselection AT wanlingao compressedsensingbasedappleimagemeasurementmatrixselection AT ganghongzhang compressedsensingbasedappleimagemeasurementmatrixselection AT hanzhang compressedsensingbasedappleimagemeasurementmatrixselection |