Abstract
Wavelet transformation is well applied in the field of image processing, and parameter optimization of wavelet transformation has always been an eternal topic on its performance improvement. In this paper, an adaptive self-organizing migration algorithm (ASOMA) is proposed to optimize the wavelet parameters to elevate the performance of wavelet denoising. Firstly, based on the original SOMA, an adaptive step size adjustment method is proposed by recording the step information of successful individuals, which improves the search ability of the SOMA. Secondly, an exploratory selection method of leader is proposed to effectively balance the exploration and exploitation of the SOMA. Finally, ASOMA is compared with the original SOMA and its variants using wavelet general threshold denoising on classical test images in denoising performance, which is evaluated by the indicators of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The experimental results demonstrate that ASOMA has better denoising performance than the wavelet general threshold, the original SOMA, and the related variants of SOMA.
Highlights
The Principle of Wavelet DenoisingBecause the value of step is fixed, the individuals cannot be effectively explored and exploited based on the current population information in the early and late stages of migration
Some scholars have combined the evolutionary algorithm (EA) model with the wavelet denoising and quickly searched for the optimal threshold through the idea of metaheuristic
El-Dahshan used GA to select the best wavelet denoising parameters to maximize the filtering performance. e verification shows that the denoising scheme with genetic algorithm has better performance than other reported wavelet threshold algorithms, and the denoising quality of ECG signals is more suitable for clinical diagnosis [24]
Summary
Because the value of step is fixed, the individuals cannot be effectively explored and exploited based on the current population information in the early and late stages of migration. In the early stage of the algorithm, a larger step value will cause the algorithm to migrate too fast, reducing the initial exploration ability of the SOMA and causing the individuals to migrate toward the local optimal direction. Rough the method of adaptive step, ASOMA uses the success information in the historical migration to adjust the step change more effectively It solves the problem of poor algorithm convergence caused by the fixed value of step and can balance the exploration and exploitation of the SOMA.
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