Publication Cover
Canadian Journal of Remote Sensing
Journal canadien de télédétection
Volume 49, 2023 - Issue 1
579
Views
1
CrossRef citations to date
0
Altmetric
Research Article

An Algorithmic Approach towards Remote Sensing Imagery Data Restoration Using Guided Filters in Real-Time Applications

Une approche algorithmique de restauration d’images de télédétection à l’aide de filtres guidés pour des applications en temps réel

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, & ORCID Icon
Article: 2257323 | Received 20 Mar 2023, Accepted 02 Sep 2023, Published online: 15 Sep 2023

References

  • Anonymous. 2014. “Test images” 2014-03-13, Website: https://ccia.ugr.es/cvg/dbimagenes/.
  • Anonymous 2023., “Dataset of standard 512X512 grayscale test images” 23/7/03, Website: https://ccia.ugr.es/cvg/CG/base.htm.
  • Bamler, R. 2000. “Principles of synthetic aperture radar.” Surveys in Geophysics, Vol. 21(No. 2/3):pp. 147–157. doi:10.1023/A:1006790026612.
  • Baraha, S., Sahoo, A.K., and Modalavalasa, S. 2022. “A systematic review on recent developments in nonlocal and variational methods for SAR image despeckling.” Signal Processing, Vol. 196: pp. 108521. doi:10.1016/j.sigpro.2022.108521.
  • Baraha, S., and Sahoo, A.K. 2022. “Restoration of speckle noise corrupted SAR images using regularization by denoising.” Journal of Visual Communication and Image Representation, Vol. 86: pp. 103546. doi:10.1016/j.jvcir.2022.103546.
  • Baraha, S., and Sahoo, A.K. 2020. “SAR image despeckling using plug‐and‐play ADMM.” IET Radar, Sonar & Navigation, Vol. 14(No. 9): pp. 1297–1309. doi:10.1049/iet-rsn.2019.0609.
  • Barman, T., Deka, B., and Mullah, H.U. 2023. “Edge-preserving single remote-sensing image super-resolution using sparse representations.” SN Computer Science, Vol. 4(No. 3): pp. 1–22. doi:10.1007/s42979-023-01764-7.
  • Yu, C., and Shin, Y. 2022. "SAR image despeckling based on U-shaped transformer from a single noisy image," 2022 13th International Conference on Information and Communication Technology Convergence (ICTC), pp. 1738–1740, Jeju Island, Republic of Korea, IEEE Xplore doi:10.1109/ICTC55196.2022.9952991.
  • Dalsasso, E., Denis, L., Muzeau, M., and Tupin, F. 2022. “Self-supervised training strategies for SAR image despeckling with deep neural networks”. In EUSAR 2022; 14th European Conference on Synthetic Aperture Radar, pp. 1–6. Leipzig, Germany: IEEE Xplore.
  • Farhadiani, R., Homayouni, S., Bhattacharya, A., and Mahdianpari, M. 2022. “SAR despeckling based on CNN and Bayesian estimator in complex wavelet domain.” IEEE Geoscience and Remote Sensing Letters, Vol. 19: pp. 1–5. doi:10.1109/LGRS.2022.3185557.
  • Hiremath, B. 2021. “All you need to know about guided image filtering”, 22/12/2021. Website: https://analyticsindiamag.com/all-you-need-to-know-about-guided-image-filtering/.
  • Hong, H.P. 2021. “Response and first passage probability of linear elastic SDOF systems subjected to nonstationary stochastic excitation modelled through S-transform.” Structural Safety, Vol. 88: pp. 102007. doi:10.1016/j.strusafe.2020.102007.
  • Iqbal, M., Chen, J., Yang, W., Wang, P., and Sun, B. 2013. “SAR image despeckling by selective 3D filtering of multiple compressive reconstructed images.” Progress in Electromagnetics Research, Vol. 134: pp. 209–226. doi:10.2528/PIER12091504.
  • He, K., Sun, J., and Tang, X. 2013. “Guided image filtering.” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35(No. 6): pp. 1397–1409. doi:10.1109/TPAMI.2012.213.
  • Kamath, P.R., Senapati, K., and Jidesh, P. 2021. “Despeckling of SAR images using shrinkage of two-dimensional discrete orthonormal S-transform.” International Journal of Image and Graphics, Vol. 21(No. 02): pp. 2150023. doi:10.1142/S0219467821500236.
  • Kamath, P. R. 2021. Some applications of S-transform and its modifications in signal and image processing. Doctoral dissertation. Surathkal: National Institute of Technology Karnataka.
  • Katunin, A. 2021. “Identification of structural damage using S-transform from 1D and 2D mode shapes.” Measurement, Vol. 173: pp. 108656. doi:10.1016/j.measurement.2020.108656.
  • Lapini, A., Bianchi, T., Argenti, F., and Alparone, L. 2014. “Blind speckle decorrelation for SAR image despeckling.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 52(No. 2): pp. 1044–1058. doi:10.1109/TGRS.2013.2246838.
  • Li, W., Pang, B., Xu, X., and Wei, B. 2022. “Multi-dictionary learning with superpixel-based clustering for SAR Image despeckling.” IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), pp. 390–393. doi:10.1109/IPEC54454.2022.9777399.
  • Li, J., Yu, W., Wang, Y., Wang, Z., Xiao, J., Yu, Z., and Zhang, D. 2023. “Guidance-aided triple-adaptive frost filter for speckle suppression in the synthetic aperture radar image.” Remote Sensing, Vol. 15(No. 3): pp. 551. doi:10.3390/rs15030551.
  • Liu, S., Pu, N., Cao, J., and Zhang, K. 2022. “Synthetic aperture radar image despeckling based on multi-weighted sparse coding.” Entropy, Vol. 24(No. 1): pp. 96. doi:10.3390/e24010096.
  • Esam El-Dine Atta, M., Ibrahim, D.K., and Gilany, M.I. 2021. “Broken bar faults detection under induction motor starting conditions using the optimized stockwell transform and adaptive time–frequency filter.” IEEE Transactions on Instrumentation and Measurement, Vol. 70: pp. 1–10. doi:10.1109/TIM.2021.3084301.
  • Mahajan, Parul. 2023. “Peak signal-to-noise ratio as an image quality metric”, Mar 23, 2023, Website: https://www.ni.com/en-in/shop/data-acquisition-and-control/add-ons-for-data-acquisition-and-control/what-is-vision-development-module/peak-signal-to-noise-ratio-as-an-image-quality-metric.html#:∼:text=The%20term%20peak%20signal%2Dto,the%20quality%20of%20its%20representation.
  • Mejjaoli, H. 2021. “Dunkl–Stockwell transform and its applications to the time–frequency analysis.” Journal of Pseudo-Differential Operators and Applications, Vol. 12(No. 2): pp. 1–59. doi:10.1007/s11868-021-00378-y.
  • Mohanakrishnan, P., Suthendran, K., Pradeep, A., and Yamini, A.P. 2022. “Synthetic aperture radar image despeckling based on modified convolution neural network.” Applied Geomatics, Vol. 14: pp. 1–12. doi:10.1007/s12518-022-00420-8.
  • Morteza, A., and Amirmazlaghani, M. 2022. “A Novel Gaussian-Copula modeling for image despeckling in the shearlet domain.” Signal Processing, Vol. 192: pp. 108340. doi:10.1016/j.sigpro.2021.108340.
  • Mv, S., and Mn, G. 2015. “A modified BM3D algorithm for SAR image despeckling.” Procedia Computer Science, Vol. 70: pp. 69–75. doi:10.1016/j.procs.2015.10.038.
  • Nabil, G., Azzedine, B., and Mustapha, B. 2023. “Fast and efficient variational method based on G 0 distribution for SAR image despeckling.” Multimedia Tools and Applications, Vol. 82(No. 4): pp. 5899–5922. doi:10.1007/s11042-022-13472-0.
  • Perera, M.V., Bandara, W.G.C., Valanarasu, J.M.J., and Patel, V.M. 2022a. “Transformer-based SAR image despeckling.” IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, pp. 751–754. doi:10.1109/IGARSS46834.2022.9884596.
  • Perera, M.V., Bandara, W.G.C., Valanarasu, J.M.J., and Patel, V.M. 2022b. “SAR despeckling using overcomplete convolutional networks.” IGARSS 2022–2022 IEEE International Geoscience and Remote Sensing Symposium, pp. 401–404. doi:10.1109/IGARSS46834.2022.9884632.
  • Perera, M.V., Nair, N.G., Bandara, W.G.C., and Patel, V.M. 2023. “Sar despeckling using a denoising diffusion probabilistic model.” IEEE Geoscience and Remote Sensing Letters, Vol. 20: pp. 1–5. doi:10.1109/LGRS.2023.3270799.
  • Stockwell, R.G., Mansinha, L., and Lowe, R.P. 1996. “Localization of the complex spectrum: The S transform.” IEEE Transactions on Signal Processing, Vol. 44(No. 4): pp. 998–1001. doi:10.1109/78.492555.
  • Shree, R., Shukla, A.K., Pandey, R.P., Shukla, V., and Singh, P. 2020. “A critical review on despeckling methods in agricultural SAR image.” International Journal of Applied Exercise Physiology, Vol. 9(No. 7): pp. 258–266.
  • Singh, P., and Shree, R. 2020a. “A new homomorphic and method noise thresholding based despeckling of SAR image using anisotropic diffusion.” Journal of King Saud University – Computer and Information Sciences, Vol. 32(No. 1): pp. 137–148. doi:10.1016/j.jksuci.2017.06.006.
  • Singh, P., Shree, R., and Diwakar, M. 2021. “A new SAR image despeckling using correlation based fusion and method noise thresholding.” Journal of King Saud University - Computer and Information Sciences, Vol. 33(No. 3): pp. 313–328. doi:10.1016/j.jksuci.2018.03.009.
  • Singh, P., Diwakar, M., Shankar, A., Shree, R., and Kumar, M. 2021. “A review on SAR image and its despeckling.” Archives of Computational Methods in Engineering, Vol. 28(No. 7): pp. 4633–4653. doi:10.1007/s11831-021-09548-z.
  • Singh, P., and Shree, R. 2020b. “Impact of method noise on SAR image despeckling.” International Journal of Information Technology and Web Engineering, Vol. 15(No. 1): pp. 52–63. doi:10.4018/IJITWE.2020010104.
  • Singh, P., and Shree, R. 2016. “Speckle noise: Modelling and implementation.” International Journal of Control Theory Applications, Vol. 9(No. 17): pp. 8717–8727.
  • Singh, P., and Shree, R. 2017a. “A new computationally improved homomorphic despeckling technique of SAR images.” International Journal of Advanced Research in Computer Science, Vol. 8(No. 3): pp. 894–898. doi:10.26483/ijarcs.v8i3.3122.
  • Singh, P., and Shree, R. 2017b. “Statistical quality analysis of wavelet based SAR images in despeckling process.” Asian Journal of Electrical Sciences, Vol. 6(No. 2): pp. 1–18. doi:10.51983/ajes-2017.6.2.2001.
  • Singh, P., Shankar, A., Diwakar, M., and Khosravi, M.R. 2022. “MSPB: intelligent SAR despeckling using wavelet thresholding and bilateral filter for big visual radar data restoration and provisioning quality of experience in real-time remote sensing.” Environment, Development and Sustainability, pp. 1–31. doi:10.1007/s10668-022-02395-3.
  • Tufa, G.T., Andargie, F.A., and Bijalwan, A. 2022. “Acceleration of Deep Neural Network Training Using Field Programmable Gate Arrays.” Computational Intelligence and Neuroscience, Vol. 2022: pp. 8387364–11. doi:10.1155/2022/8387364.
  • Wang, C., Yin, Z., Ma, X., and Yang, Z. 2022. “SAR image despeckling based on block-matching and noise-referenced deep learning method.” Remote Sensing, Vol. 14(No. 4): pp. 931. doi:10.3390/rs14040931.
  • Wang, G., Bo, F., Chen, X., Lu, W., Hu, S., and Fang, J. 2022. “A collaborative despeckling method for SAR images based on texture classification.” Remote Sensing, Vol. 14(No. 6): pp. 1465. doi:10.3390/rs14061465.
  • Wu, F., Zhu, C., Xu, J., Bhatt, M.W., and Sharma, A. 2022. “Research on image text recognition based on canny edge detection algorithm and k-means algorithm.” International Journal of System Assurance Engineering and Management, Vol. 13(No. S1): pp. 72–80. doi:10.1007/s13198-021-01262-0.
  • Wu, W., Huang, X., Shao, Z., Teng, J., and Li, D. 2022. “SAR-DRDNet: a SAR image despeckling network with detail recovery.” Neurocomputing, Vol. 493: pp. 253–267. doi:10.1016/j.neucom.2022.04.066.
  • Yommy, A. S., Liu, R., and Wu, S. 2015. “SAR image despeckling using refined Lee filter”. 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, Vol. 2: pp. 260–265. China: IEEE Xplore
  • Zhou, P., Ye, W., Xia, Y., and Wang, Q. 2011. “An improved canny algorithm for edge detection.” Journal of Computational Information Systems, Vol. 7(No. 5): pp. 1516–1523.
  • Wang, Z., Bovik, A.C., Sheikh, H.R., and Simoncelli, E.P. 2004. “Image quality assessment: from error visibility to structural similarity.” IEEE Transactions on Image Processing, Vol. 13(No. 4): pp. 600–612. doi:10.1109/TIP.2003.819861.
  • Wang, Z., and Bovik, A.C. 2002. “A universal image quality index," in.” IEEE Signal Processing Letters, Vol. 9(No. 3): pp. 81–84. doi:10.1109/97.995823.
  • Zhu, J., Wen, J., and Zhang, Y. 2013. “A new algorithm for SAR image despeckling using an enhanced Lee filter and median filter”. In 2013 6th International congress on image and signal processing (CISP), Vol. 1, pp. 224–228. Hangzhou, China. IEEE Xplore doi:10.1109/CISP.2013.6743991.