Abstract
With the rapid development of multimedia technology, it is very important to improve the accuracy of sparse signal reconstruction for image or speech based on compressed sensing technology. In this work, a matching pursuit algorithm for sparse signal reconstruction based on Jaccard coefficient and backtracking strategy is proposed to improve the accurate reconstruction probability. Jaccard coefficient matching criterion and inner product matching criterion are combined to preserve more features of the original vector. A mean-based thresholding strategy is used to selects atoms adaptively, and the backtracking method is introduced to filter out the wrong atoms. Experimental results show that the reconstruction performance of the proposed algorithm is much better than other greedy algorithms when the sparsity K is large (K>35). For speech signals, the proposed algorithm has better reconstruction performance than other algorithms for both male and female voice signals. Under different compression ratios, the AFSNR and the PESQ scores of the reconstructed speech by the proposed algorithm are increased by at least 1.5348 dB and 0.5629, respectively, especially showing superior performance under larger compression ratio. The proposed algorithm has bright application prospect in intelligent speech interaction with accurate reconstruction of speech signal under large compression ratios.
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Acknowledgements
We would like to thank the editors and reviewers for their comments and suggestions. This work was supported in part by Science and technology strategic cooperation project of Nanchong City and School (Grant SXHZ020).
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Li, Z., Zheng, X., Chen, G. et al. A Matching Pursuit Algorithm for Sparse Signal Reconstruction Based on Jaccard Coefficient and Backtracking. Circuits Syst Signal Process 42, 6210–6227 (2023). https://doi.org/10.1007/s00034-023-02396-1
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DOI: https://doi.org/10.1007/s00034-023-02396-1