Nilas, Phongchai
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Preferred name
Nilas, Phongchai
Alternative Name
Nilas, P.
Main Affiliation
Email
phongchai.ni@kmitl.ac.th
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Item type:Publication, Speckle noise reduction using adaptive singular value decomposition in logarithmic domain(2005-12-01); ; Cheevasuvit, F.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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Contrast enhancement of satellite image based on adaptive unsharp masking using wavelet transform(2006-12-01); ; Cheevasuvit, F.This paper concerns with a method for unsharp masking for contrast enhancement of satellite image. We employ the nature of wavelet transform that separates the original image into low and high frequency sub-band images as low and high pass filter. Particularly, a number of high frequency sub-band images consist of horizontal, vertical, and diagonal coefficients that contain detail of information. Taking inverse wavelet transform of each sub-band image separately except low frequency one, we have each of high frequency information in horizontal, vertical, and diagonal image. All of them are scaled by the scaling factor in each one separately. Adaptive algorithm is implemented to results the suitable scaling factor to obtain the enhanced image corresponding with the given criterion based on variance of each area smooth and detail area. Experimental results show that our method performs well to high enhance in detail area and low in smooth area.