@inproceedings{scholars2760,
             doi = {10.1109/ICIAS.2012.6306098},
            note = {cited By 0; Conference of 2012 4th International Conference on Intelligent and Advanced Systems, ICIAS 2012 ; Conference Date: 12 June 2012 Through 14 June 2012; Conference Code:93534},
          volume = {2},
         address = {Kuala Lumpur},
           title = {Speckle reduction of SAR images based on signal subspace technique},
            year = {2012},
           pages = {670--675},
         journal = {ICIAS 2012 - 2012 4th International Conference on Intelligent and Advanced Systems: A Conference of World Engineering, Science and Technology Congress (ESTCON) - Conference Proceedings},
            isbn = {9781457719677},
          author = {Yahya, N. and Kamel, N. S. and Malik, A. S.},
        abstract = {In this paper, speckle removal from synthetic aperture radar (SAR) images using subspace-based technique is proposed. The fundamental principle is to decompose the vector space of the noisy image into signal-plus-noise subspace and the noise subspace. Noise reduction is achieved by removing the noise subspace and estimating the clean image from the remaining image subspace. Linear estimation of the clean image is performed by minimizing image distortion while maintaining the residual noise energy below some given threshold. Since the noise is considered to be additive with subspace technique, a homomorphic framework is used to convert the multiplicative speckle noise into additive. The performance of the proposed approach is tested with simulated images and with real SAR images, and compared with Lee filter. The results indicated significant improvements by the proposed technique in terms of structural similarity index measure (SSIM) and equivalent number of looks (ENL). {\^A}{\copyright} 2011 IEEE.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867932982&doi=10.1109\%2fICIAS.2012.6306098&partnerID=40&md5=f27cfe6b6f30f71e11febb6259264530},
        keywords = {Equivalent number of looks; Fundamental principles; Image distortions; Linear estimation; Multiplicative speckle noise; Noise subspace; Noisy image; Residual noise; SAR Images; Signal sub-space; Simulated images; Speckle noise; Speckle reduction; Speckle removal; Structural similarity; Subspace techniques; Synthetic aperture radar (SAR) images, Signal analysis; Speckle; Synthetic aperture radar, Image denoising}
}