Lee, J.S. A simple speckle smoothing algorithm for synthetic aperture radar images. IEEE Transactions on Systems, Man and Cybernetics, SMC-13:85-89, 1983. Home/Search Document Not in Database Summary Related Articles Check |
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A Neural Network For Enhancing Boundaries And Surfaces.. - Mingolla, Ross.. (1998) (4 citations) (Correct)
....tends to reduce local image contrasts in order to provide display capability over the full range of possible sensor values. Simple statistical models for signal and noise have also been used to reduce speckle. These models typically sacrifice a portion of the signal in order to eliminate noise (Lee, 1983). Iterative smoothing techniques have also been used. These techniques suffer from insensitivity to scenic structure and tend to blur the boundaries between surfaces, especially if they are iterated too often. The number of iterations needed for the best results may depend upon the image being ....
....structure and tend to blur the boundaries between surfaces, especially if they are iterated too often. The number of iterations needed for the best results may depend upon the image being processed. This limitation is illustrated for SAR processing through adaptive averaging with a sigma filter (Lee, 1983) and a geometric filter (Crimmins, 1985) by Grossberg, Mingolla, and Williamson (1995) Our approach further develops the use of surface contrast enhancement, normalization, and diffusive filling in between form sensitive boundary signals as a technique for SAR image enhancement, as described by ....
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Lee, J. (1983). A simple speckle smoothing algorithm for synthetic aperture radar images.
Nonlinear Adaptive Filters for Speckle Suppression .. - Kofidis.. (1996) (1 citation) (Correct)
....mean to the standard deviation of the envelope, equals 1. 91, a low value which reflects the importance of this noise source [18] A multiplicative model for the noise is implied by the fact that the standard deviation is directly proportional to the mean and it has been verified experimentally [13]. 1 The poor quality of the US images, due to speckle artifacts, degrades their clinical information content, preventing accurate diagnosis. Experimental results reported in [24] indicated that, although the resolution of the imaging system is the limiting factor for the detection of ....
....[24] indicated that, although the resolution of the imaging system is the limiting factor for the detection of high contrast lesions, it is the speckle noise that limits the detection of low contrast lesions. Several algorithms have been proposed aiming at reducing speckle noise in images (e.g. [3, 13, 16, 19, 15, 34]) Since the ultimate goal of any speckle suppression scheme should be the reduction of speckle contrast to enhance the diagnostic value of the image, edge and detail preservation are crucial in a speckle filter along with noise reduction. Thus, spatiallyvarying filters are required that are also ....
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J.-S. Lee, "A Simple Speckle Smoothing Algorithm for Synthetic Aperture Radar Images, " IEEE Trans. Systems, Man, and Cybernetics, vol. 13, no. 1, Jan/Feb. 1983, pp. 85--89.
Models for Synthetic Aperture Radar Image Analysis - Frery, Correia, Renno.. (1999) (Correct)
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Lee, J.S. A simple speckle smoothing algorithm for synthetic aperture radar images. IEEE Transactions on Systems, Man and Cybernetics, SMC-13:85-89, 1983.
Bayesian Neural Networks Based Evaluation of Binary Speckle.. - Toussaint, Gori, Dose (2004) (Correct)
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J. S. Lee, " A simple speckle smoothing algorithm for synthetic aperture radar images," in IEEE Trans. System, Man, and Cybernetics SMC-13, (1983) P. 85-89. 14
Speckle Noise MAP Filtering Based on Local Adaptive.. - Medeiros.. (1999) (Correct)
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J. S. Lee, "A simple speckle smoothing algorithm for synthetic aperture radar images", IEEE Trans. on System, Man and Cybernetics, SMC-13 (1): 85-89, 1983.
Support To The Evaluation Of The Short-Term Exploitation - Opportunities For Ers (Correct)
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J.S. Lee, "A simple speckle smoothing algorithm for synthetic aperture radar images", IEEE Transactions on Systems, Man and Cybernetics, 13, pp.85 -- 89, 1983.
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