Optimized Wavelet-Based Satellite Image De-Noising With Multi-Population Differential Evolution-Assisted Harris Hawks Optimization Algorithm
In this research, we propose to utilize the newly introduced Multi-population differential evolution-assisted Harris Hawks Optimization Algorithm (CMDHHO) in the optimization process for satellite image denoising in the wavelet domain. This optimization algorithm is the improved version of the previ...
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9143087/ |
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| author | Noorbakhsh Amiri Golilarz Mirpouya Mirmozaffari Tayyebeh Asgari Gashteroodkhani Liaqat Ali Hamidreza Ahady Dolatsara Azam Boskabadi Mohammad Yazdi |
| author_facet | Noorbakhsh Amiri Golilarz Mirpouya Mirmozaffari Tayyebeh Asgari Gashteroodkhani Liaqat Ali Hamidreza Ahady Dolatsara Azam Boskabadi Mohammad Yazdi |
| author_sort | Noorbakhsh Amiri Golilarz |
| collection | DOAJ |
| description | In this research, we propose to utilize the newly introduced Multi-population differential evolution-assisted Harris Hawks Optimization Algorithm (CMDHHO) in the optimization process for satellite image denoising in the wavelet domain. This optimization algorithm is the improved version of the previous HHO algorithm which consists of chaos, multi-population, and differential evolution strategies. In this study, we applied several optimization algorithms in the optimization procedure and we compared the de-noising results with CMDHHO based noise suppression as well as with the Thresholding Neural Network (TNN) approaches. It is observed that applying the CMDHHO algorithm provides us with better qualitative and quantitative results comparing with other optimized and TNN based noise removal techniques. In addition to the quality and quantity improvement, this method is computationally efficient and improves the processing time. Based on the experimental analysis, optimized based noise suppression performs better than TNN based image de-noising. Peak Signal to Noise Ratio (PSNR) and Mean Structural Similarity Index (MSSIM) are used to evaluate and measure the performance of different de-noising methods. Experimental results indicate the superiority of the proposed CMDHHO based satellite image de-noising over other available approaches in the literature. |
| first_indexed | 2024-12-19T07:34:06Z |
| format | Article |
| id | doaj.art-01cf297aca194f2d9a8d80e6873c35a3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:34:06Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-01cf297aca194f2d9a8d80e6873c35a32022-12-21T20:30:36ZengIEEEIEEE Access2169-35362020-01-01813307613308510.1109/ACCESS.2020.30101279143087Optimized Wavelet-Based Satellite Image De-Noising With Multi-Population Differential Evolution-Assisted Harris Hawks Optimization AlgorithmNoorbakhsh Amiri Golilarz0https://orcid.org/0000-0003-2676-989XMirpouya Mirmozaffari1Tayyebeh Asgari Gashteroodkhani2Liaqat Ali3https://orcid.org/0000-0002-3095-7271Hamidreza Ahady Dolatsara4Azam Boskabadi5Mohammad Yazdi6School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Industrial, Manufacturing, and Systems Engineering, The University of Texas at Arlington, Arlington, TX, USADepartment of Electrical Engineering, University of Guilan, Rasht, IranSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Management, Clark University, Worcester, MA, USADepartment of Finance and Management Science, Carson College of Business, Washington State University, Pullman, WA, USAInstituto Superior Técnico, University of Lisbon, Lisbon, PortugalIn this research, we propose to utilize the newly introduced Multi-population differential evolution-assisted Harris Hawks Optimization Algorithm (CMDHHO) in the optimization process for satellite image denoising in the wavelet domain. This optimization algorithm is the improved version of the previous HHO algorithm which consists of chaos, multi-population, and differential evolution strategies. In this study, we applied several optimization algorithms in the optimization procedure and we compared the de-noising results with CMDHHO based noise suppression as well as with the Thresholding Neural Network (TNN) approaches. It is observed that applying the CMDHHO algorithm provides us with better qualitative and quantitative results comparing with other optimized and TNN based noise removal techniques. In addition to the quality and quantity improvement, this method is computationally efficient and improves the processing time. Based on the experimental analysis, optimized based noise suppression performs better than TNN based image de-noising. Peak Signal to Noise Ratio (PSNR) and Mean Structural Similarity Index (MSSIM) are used to evaluate and measure the performance of different de-noising methods. Experimental results indicate the superiority of the proposed CMDHHO based satellite image de-noising over other available approaches in the literature.https://ieeexplore.ieee.org/document/9143087/CMDHHOoptimization algorithmsatellite image de-noisingTNNwavelet domain |
| spellingShingle | Noorbakhsh Amiri Golilarz Mirpouya Mirmozaffari Tayyebeh Asgari Gashteroodkhani Liaqat Ali Hamidreza Ahady Dolatsara Azam Boskabadi Mohammad Yazdi Optimized Wavelet-Based Satellite Image De-Noising With Multi-Population Differential Evolution-Assisted Harris Hawks Optimization Algorithm IEEE Access CMDHHO optimization algorithm satellite image de-noising TNN wavelet domain |
| title | Optimized Wavelet-Based Satellite Image De-Noising With Multi-Population Differential Evolution-Assisted Harris Hawks Optimization Algorithm |
| title_full | Optimized Wavelet-Based Satellite Image De-Noising With Multi-Population Differential Evolution-Assisted Harris Hawks Optimization Algorithm |
| title_fullStr | Optimized Wavelet-Based Satellite Image De-Noising With Multi-Population Differential Evolution-Assisted Harris Hawks Optimization Algorithm |
| title_full_unstemmed | Optimized Wavelet-Based Satellite Image De-Noising With Multi-Population Differential Evolution-Assisted Harris Hawks Optimization Algorithm |
| title_short | Optimized Wavelet-Based Satellite Image De-Noising With Multi-Population Differential Evolution-Assisted Harris Hawks Optimization Algorithm |
| title_sort | optimized wavelet based satellite image de noising with multi population differential evolution assisted harris hawks optimization algorithm |
| topic | CMDHHO optimization algorithm satellite image de-noising TNN wavelet domain |
| url | https://ieeexplore.ieee.org/document/9143087/ |
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