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Volume 46 Issue 4
Apr.  2024
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CHEN Pingping, CHEN Jiahui, WANG Xuanda, FANG Yi, WANG Feng. Regular Backtracking Fast Orthogonal Matching Pursuit Algorithm Based on Dice Coefficient Forward Prediction[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1488-1498. doi: 10.11999/JEIT230558
Citation: CHEN Pingping, CHEN Jiahui, WANG Xuanda, FANG Yi, WANG Feng. Regular Backtracking Fast Orthogonal Matching Pursuit Algorithm Based on Dice Coefficient Forward Prediction[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1488-1498. doi: 10.11999/JEIT230558

Regular Backtracking Fast Orthogonal Matching Pursuit Algorithm Based on Dice Coefficient Forward Prediction

doi: 10.11999/JEIT230558
Funds:  The National Natural Science Foundation of China (62171135, 62071131), Fujian Distinguished Talent Project (2022J06010), The Key Project of Education Department (2023XQ004), Quanzhou Sci-Tech Project (2021N050)
  • Received Date: 2023-06-10
  • Rev Recd Date: 2023-09-22
  • Available Online: 2023-10-18
  • Publish Date: 2024-04-24
  • In order to improve the success rate and reconstruction accuracy of the compressed sensing reconstruction algorithm, the Look Ahead and Regular Backtracking Orthogonal Matching Pursuit based on Dice coefficient (DLARBOMP) is proposed. In this algorithm, from the perspective of matching criteria and atom selection in the pre-selection stage, the Dice coefficient is used to replace the atomic inner product to calculate the correlation value and preserve the characteristics of the original signal, to select the atom that best matches the residual and improve the reconstruction accuracy. At the same time, to reduce backtracking time in the reconstruction process, regularization is used to select multiple atoms instead of a single atom in each iteration, achieving a balance between reconstruction accuracy and time. Finally, the experimental results of sparse one-dimensional signal and two-dimensional image signal reconstruction show that the proposed DLARBOMP algorithm considers both performance and efficiency when reconstructing one-dimensional signal, and enhances the Peak Signal-to-Noise Ratio (PSNR) when reconstructing two-dimensional compressed image signal, as compared to Orthogonal Matching Pursuit (OMP) and the state-of-the-art greedy algorithms.
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