Volume 32 Issue 1
Jan.  2023
Turn off MathJax
Article Contents
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
Citation: 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

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

doi: 10.23919/cje.2021.00.138
More Information
  • 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
  • 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.
  • loading
  • [1]
    M. Usman and D. Armitage, “Details of an imaging system based on reflected GPS signals and utilizing SAR techniques,” Journal of Global Positioning Systems, vol.8, no.1, pp.87–99, 2009. doi: 10.5081/jgps.8.1.87
    W. -N. Chen, J. Zhang, H. S. Chung, et al., “A novel set-based particle swarm optimization method for discrete optimization problems,” IEEE Transactions on Evolutionary Computation, vol.14, no.2, pp.278–300, 2010. doi: 10.1109/TEVC.2009.2030331
    C. Oliver and S. Quegan, Understanding Synthetic Aperture Radar Images, SciTech Publishing, 2004.
    C. R. Jackson and S. W. McCandless Jr., “Principles of synthetic aperture radar,” in Synthetic Aperture Radar Marine User’s Manual, U.S. Department of Commerce, National Oceanic and Atmospheric Administration, p.11, 2004.
    A. Komjathy, J. Maslanik, V. Zavorotny, P. Axelrad, and S. Katzberg, “Towards GPS surface reflection remote sensing of sea ice conditions,” in Proceedings of 6th International Conference on Remote Sensing for Marine and Coastal Environments, Charleston, SC, USA, article no.20010124078, 2000.
    D. Masters, S. Katzberg, and P. Axelrad, “Airborne GPS bistatic radar soil moisture measurements during SMEX02,” in Proceedings of 2003 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2003), Toulouse, France, pp.896–898, 2003.
    R. Cheng and Y. Jin, “A competitive swarm optimizer for large scale optimization,” IEEE Transactions on Cybernetics, vol.45, no.2, pp.191–204, 2015. doi: 10.1109/TCYB.2014.2322602
    J. Kennedy, “Particle swarm optimization,” in Encyclopedia of Machine Learning, Springer, pp.60–766, 2011.
    M. A. Mansoori, M. R. Mosavi, and M. H. Bisjerdi, “Improved regularization based blind image de-convolution using PSO algorithm for PMMW images application,” in Proc. of 2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP), Tehran, Iran, pp.33–36, 2015.
    T. Y. Sun, C. C. Liu, Y. P. Jheng, J. H. Jheng, S. J. Tsai, and S. T. Hsieh, “Blind image deconvolution via particle swarm optimization with entropy evaluation,” in Proceedings of 2008 8th International Conference on Intelligent Systems Design and Applications, Kaohsuing, China, pp.265–270, 2008.
    G. R. Mamta and M. Dutta, “Pso based blind deconvolution technique of image restoration using cepstrum domain of motion blur,” in Computational Vision and Bio Inspired Computing, Springer, pp.947–958, 2018.
    J. Tindall, “Deconvolution of plant type(s) for homeland security enforcement using remote sensing on a UAV collection platform,” Homeland Security Affairs, vol.2, article no.4, 2006.
    T. Zeng, M. Cherniakov, and T. Long, “Generalized approach to resolution analysis in BSAR,” IEEE Transactions on Aerospace and Electronic Systems, vol.41, no.2, pp.461–474, 2005. doi: 10.1109/TAES.2005.1468741
    W.-Q. Wang, “GPS-based time & phase synchronization processing for distributed SAR,” IEEE Transactions on Aerospace and Electronic Systems, vol.45, no.3, pp.1040–1051, 2009.
    D. C. Munson and R. L. Visentin, “A signal processing view of strip-mapping synthetic aperture radar,” IEEE Transactions on Acoustics, Speech. and Signal Processing, vol.37, no.12, pp.2131–2147, 1989. doi: 10.1109/29.45556
    G. Cardillo, “On the use of the gradient to determine bistatic sar resolution,” in Proceedings of International Symposium on Antennas and Propagation Society, Merging Technologies for the 90’s, Dallas, TX, USA, pp.1032–1035, 1990.
    U. Javed, M. M. Riaz, and T. A. Cheema, “Multichannel blind image deconvolution,” in Proc. of 2012 9th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan, pp.95–99, 2012.
    M. Usman and D. Armitage, “Acquisition of reflected GPS signals for remote sensing applications,” in Proceedings of the 2nd International Conference on Advances in Space Technologies (ICAST), Islamabad, Pakistan, pp.131–136, 2008
    S. Saadi, A. Kouzou, A. Guessoum, and M. Bettayeb, “A comparative study to select an image deconvolution method,” in Proceedings of 2010 7th International Multi-Conference on Systems Signals and Devices (SSD) , Amman, Jordan, pp.1–6, 2010.
    F. Santi, M. Antoniou, and D. Pastina, “Point spread function analysis for GNSS-based multistatic SAR,” IEEE Geoscience and Remote Sensing Letters, vol.12, no.2, pp.304–308, 2015. doi: 10.1109/LGRS.2014.2337054
    R. C. Gonzalez and R. E. Woods, Digital Image Processing, Addison-Wesley, Boston, 1992.
    R. L. Moses and J. N. Ash, “Recursive SAR imaging,” in Proceedings of SPIE Defense and Security Symposium, Orlando, Florida, USA, DOI: 10.1117/12.786307, 2008.
    J. -L. Starck, E. Pantin, and F. Murtagh, “Deconvolution in astronomy: A review,” Publications of the Astronomical Society of the Pacific, vol.114, no.800, article no.1051, 2002. doi: 10.1086/342606
    P. Campisi and K. Egiazarian, Blind Image Deconvolution: Theory and Applications. CRC Press, 2017.
    W. Hu, W. Wang, J. Ji, and L. Si, “The spatial resolution enhancement deconvolution technique of the optimized wiener filter in terahertz band,” in Proceedings of 2016 IEEE 9th UK-Europe-China Workshop on Millimetre Waves and Terahertz Technologies, Qingdao, China, pp.96–99, 2016.
    P. C. Hansen, J. G. Nagy, and D. P. O’leary, Deblurring Images: Matrices, Spectra, and Filtering, SIAM, 2006.
    A. K. Mir, M. Zubair, and I. M. Qureshi, “Lifetime maximization of wireless sensor networks using particle swarm optimization,” Turkish Journal of Electrical Engineering & Computer Sciences, vol.24, no.1, pp.160–170, 2016.
    M. M. Riaz and A. Ghafoor, “Principle component analysis and fuzzy logic based through wall image enhancement,” Progress in Electromagnetics Research, vol.127, pp.461–478, 2012. doi: 10.2528/PIER12012702
    M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis, and Machine Vision, 4th ed., Cengage Learning, 2014.
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(10)  / Tables(3)

    Article Metrics

    Article views (429) PDF downloads(39) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint