Ten lectures on wavelets
Image processing, theory, algorithms and architectures
Image processing, theory, algorithms and architectures
Adaptive thresholding of wavelet coefficients
Computational Statistics & Data Analysis
A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Image enhancement based on a nonlinear multiscale method
IEEE Transactions on Image Processing
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
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This paper describes an efficient and adaptive method of threshold estimation for removing Speckle noise from Synthetic Aperture Radar (SAR) images, based on Undecimated Double Density Wavelet Transform (UDDWT). Here the performance of image denoising algorithm is well improved by fixing different optimum threshold values for each wavelet coefficient. The choice of the estimation of the threshold value is carried out by analyzing the statistical parameters of the wavelet subband coefficients like Arithmetic Mean, Geometric Mean and Standard Deviation. Here the image is first decomposed into many subbands using UDDWT. Then based upon the statistical parameters of the wavelet coefficients of subbands, threshold values are found out for each wavelet coefficients. This threshold value is used in Soft Thresholding Technique to remove the noisy wavelet coefficients. Then the inverse transform is applied to get the denoised image. Evaluation parameters like peak signal to noise ratio, standard deviation to mean ratio and Edge Preservation Factor have been used for evaluating the performance of the proposed technique quantitatively. Experimental results on several benchmark images by using the proposed method show that, the proposed method yields significantly superior image quality. Some comparisons with the best available results will be given in order to illustrate the effectiveness of the proposed algorithm.