Speckle noise reduction using adaptive singular value decomposition in logarithmic domain - Chitwong, S.; Thongsila, A.; Intajag, S.; Nilas, P.; Cheevasuvit, F. (My University, 2005)
This paper presents applying the singular value decomposition to reduce speckle noise. Generally, it is used to filter the additive Gaussian noise with zero mean and any variance. Since speckle noise is in multiplicative model, to transform multiplicative model into additive model, we then employ logarithmic transformation. In this paper, speckle noise is generally modeled as Gamma distribution function corresponding with speckle noise of synthetic aperture radar (SAR) imagery applied. All singular value decomposition based filtering processing is in logarithmic domain. Threshold value to determine the effective rank and orders of matrix are adapted as homogeneity analysis. The orders of matrix are consisted of 16 by 16, 8 by 8 and 4 by 4. Normally, the results of the singular value decomposition based filtering after that the filtered matrix is transformed into spatial domain by exponential function is in block-fashion, then blocking effect is occurred. To smooth, the filtered matrix is performed as average filtering by using a number of pixels of 4 by 4 pixels around center of one. Experiments are tested using both simulated image and real image. Signal to noise ratio and equivalent number of looks are employed to evaluate the performance of our method. Our results are good enough when compared with the recent results at which such method is more complex. © 2005 by the American Society for Photogrammetry and Remote Sensing.