Abstract
The fixed step size in the sparse adaptive matching pursuit algorithm can result in limited accuracy and overestimation. To address this, this paper proposes a variable-step sparse adaptive matching pursuit algorithm based on the Spearman correlation coefficient. By measuring the Spearman correlation coefficient between the candidate set and the input signal, and introducing an adaptive step size adjustment method based on the parameter values of the correlation coefficient, the performance of the SAMP algorithm is optimized and its adaptability is enhanced. Extensive experiments demonstrate that the proposed method achieves good reconstruction results for one-dimensional sparse signals and two-dimensional images.
Published Version
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