Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm
Aiming at the poor effect of traditional denoising algorithms on image enhancement with strong noise, an image denoising algorithm based on improved whale optimization algorithm and parameter adaptive array stochastic resonance is proposed in the paper. In this algorithm, through dimensionality redu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/23/12084 |
| _version_ | 1827643441672093696 |
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| author | Weichao Huang Ganggang Zhang Shangbin Jiao Jing Wang |
| author_facet | Weichao Huang Ganggang Zhang Shangbin Jiao Jing Wang |
| author_sort | Weichao Huang |
| collection | DOAJ |
| description | Aiming at the poor effect of traditional denoising algorithms on image enhancement with strong noise, an image denoising algorithm based on improved whale optimization algorithm and parameter adaptive array stochastic resonance is proposed in the paper. In this algorithm, through dimensionality reduction scanning, coding, modulation and other processing, the noise-containing gray image becomes a one-dimensional aperiodic binary pulse amplitude modulation signal suitable for a bistable stochastic resonance model. Then, the traditional whale optimization algorithm is improved in the initial solution distribution, global search ability and population diversity generalization. The improved whale optimization algorithm is applied to select the parameters of the stochastic resonance, which effectively improves the parameters self-adaptive of the array stochastic resonance model. Finally, the denoised image is obtained by demodulating, decoding and anti-scanning the stochastic resonance output. The experimental results show that compared with the array stochastic resonance method with fixed parameters and the classical image denoising method, the algorithm proposed in this paper has better performance in terms of visual effect and peak signal-to-noise ratio index, which proves the advantages and effective application of the method in image denoising. |
| first_indexed | 2024-03-09T17:54:22Z |
| format | Article |
| id | doaj.art-9736f7d1b30943d9b0dc14c60caf7af6 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:54:22Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9736f7d1b30943d9b0dc14c60caf7af62023-11-24T10:30:41ZengMDPI AGApplied Sciences2076-34172022-11-0112231208410.3390/app122312084Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization AlgorithmWeichao Huang0Ganggang Zhang1Shangbin Jiao2Jing Wang3Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, ChinaShannxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Printing, Packaging and Digital Media, Xi’an University of Technology, Xi’an 710048, ChinaAiming at the poor effect of traditional denoising algorithms on image enhancement with strong noise, an image denoising algorithm based on improved whale optimization algorithm and parameter adaptive array stochastic resonance is proposed in the paper. In this algorithm, through dimensionality reduction scanning, coding, modulation and other processing, the noise-containing gray image becomes a one-dimensional aperiodic binary pulse amplitude modulation signal suitable for a bistable stochastic resonance model. Then, the traditional whale optimization algorithm is improved in the initial solution distribution, global search ability and population diversity generalization. The improved whale optimization algorithm is applied to select the parameters of the stochastic resonance, which effectively improves the parameters self-adaptive of the array stochastic resonance model. Finally, the denoised image is obtained by demodulating, decoding and anti-scanning the stochastic resonance output. The experimental results show that compared with the array stochastic resonance method with fixed parameters and the classical image denoising method, the algorithm proposed in this paper has better performance in terms of visual effect and peak signal-to-noise ratio index, which proves the advantages and effective application of the method in image denoising.https://www.mdpi.com/2076-3417/12/23/12084image denoisingimproved whale optimization algorithmarray stochastic resonancepeak signal-to-noise ratio |
| spellingShingle | Weichao Huang Ganggang Zhang Shangbin Jiao Jing Wang Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm Applied Sciences image denoising improved whale optimization algorithm array stochastic resonance peak signal-to-noise ratio |
| title | Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm |
| title_full | Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm |
| title_fullStr | Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm |
| title_full_unstemmed | Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm |
| title_short | Gray Image Denoising Based on Array Stochastic Resonance and Improved Whale Optimization Algorithm |
| title_sort | gray image denoising based on array stochastic resonance and improved whale optimization algorithm |
| topic | image denoising improved whale optimization algorithm array stochastic resonance peak signal-to-noise ratio |
| url | https://www.mdpi.com/2076-3417/12/23/12084 |
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