De-convolution and De-noising of SAR Based GPS Images Using Hybrid Particle Swarm Optimization

Rizwan Sadiq; Muhammad Bilal Qureshi; Muhammad Mohsin Khan

doi:10.23919/cje.2021.00.138

Volume 32 Issue 1

Jan. 2023

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Rizwan Sadiq, Muhammad Bilal Qureshi, Muhammad Mohsin Khan, “De-convolution and De-noising of SAR Based GPS Images Using Hybrid Particle Swarm Optimization,” Chinese Journal of Electronics, vol. 32, no. 1, pp. 166-176, 2023, doi: 10.23919/cje.2021.00.138

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Rizwan Sadiq, Muhammad Bilal Qureshi, Muhammad Mohsin Khan, “De-convolution and De-noising of SAR Based GPS Images Using Hybrid Particle Swarm Optimization,” Chinese Journal of Electronics, vol. 32, no. 1, pp. 166-176, 2023, doi: 10.23919/cje.2021.00.138

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De-convolution and De-noising of SAR Based GPS Images Using Hybrid Particle Swarm Optimization

doi: 10.23919/cje.2021.00.138

1.
Dept. of Electrical and Electronic Engineering, Koc University, Istanbul, Turkey
2.
Dept. of Electrical and Computer Engineering, COMSATS University, Islamabad, Abbottabad Campus, Pakistan
3.
Dept. of Radiology and Biomedical Imaging, Yale University, CT, USA
4.
Sino-Pak Center for Artificial Intelligence (SPCAI), Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology (PAF-IAST), Haripur, Pakistan

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Author Bio:
Sadiq Rizwan received the B.S. degree in computer engineering from COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan, in 2007 and M.S. degree in electronics engineering from International Islamic University Islamabad, Pakistan, in 2011, respectively. He received the Ph.D. degree from Koc University, Istanbul, Turkey, in 2020. Previously, he was serving as a Lecturer at COMSATS Islamabad (Abbottabad), Pakistan. His research interest includes remote sensing, machine learning, computer vision, and natural language processing. (Email: rsadiq13@ku.edu.tr)

Bilal Qureshi Muhammad received the B.S. and M.S. degrees in computer engineering from COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan, in 2007 and 2010, respectively. He received the Ph.D. degree from North Dakota State University, Fargo, ND, USA, in 2017. He is currently working as an Assistant Professor in Electrical and Computer Engineering Department in COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan. His research interests include control systems, optimization, and biomedical engineering. (Email: bilalqureshi@cuiatd.edu.pk)

Mohsin Khan Muhammad (corresponding author) has done post-doctorate research from Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA. He did his Ph.D. in Split degree program i.e. course work from IIUI and research from Queen Merry University (QMU), London, UK, in 2018. Currently, he is an Assistant Professor at Sino-Pak Center for Artificial Intelligence (SPCAI) Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology (PAF-IAST), Haripur, Pakistan. His research interest is to apply artificial intelligence and machine learning techniques to different domains of IOT, sensor networks, and biomedical. (Email: bilalqureshi@cuiatd.edu.pk)
Received Date: 2021-04-20
Accepted Date: 2022-03-28

Available Online: 2022-07-02

Publish Date: 2023-01-05

Abstract

Abstract

Synthetic aperture radar (SAR) imaging is an efficient strategy which exploits the properties of microwaves to capture images. A major concern in SAR imaging is the reconstruction of image from back scattered signals in the presence of noise. The reflected signal consist of more noise than the target signal and it is a challenging problem to reduce the noise in the collected signal for better reconstruction of an image. Current studies mostly focus on filtering techniques for noise removal. This can result in an undesirable point spread function causing extreme smearing effect in the desired image. In order to handle this problem, a computational technique, particle swarm optimization (PSO) is used for de-noising purpose and later the target performance is further improved by an amalgamation of Wiener filter. Moreover, to improve the de-noising performance we have exploited the singular value decomposition based morphological filtering. To justify the proposed improvements we have simulated the proposed techniques and results are compared with the conventional existing models. The proposed method revealed considerable decrease in mean square error compared to Wiener filter and PSO techniques. Quantitative analysis of image restoration quality are also presented in comparison with Wiener filter and PSO based on the improvement in signal to noise ratio and peak signal to noise ratio.
- Remote sensing,
- Synthetic aperture radar imaging,
- Wiener filter,
- Particle swarm optimization

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References(29)

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