A Novel Wavelet-Based Denoising Method of SAR Image Using Interscale Dependency

This paper attempts to undertake the study of two types of noise such as Salt and Pepper (SPN), Speckle (SPKN). Different noise densities have been removed by using four types of filters as meidan filter, Lee filter, Kuan filter, Frost filter, and Wavelet based Bivariate Shrinkage function. Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of the scattering phenomenon. Multiwavelet transform technique has a big advantage over the other techniques that it less distorts spectral characteristics of the image denoising We apply the proposed method for speckle SAR images by using logarithmic transformation. We present a novel approach to estimating the mean square error (MSE) associated with any given threshold level in both hard and soft thresholding This paper proposes different filtering techniques based on statistical methods for the removal of speckle noise.. The quality of the enhanced images is measured by the statistical quantity measures: Noise Variance, Mean Square Error (MSE), Equivalent Numbers of Looks (ENL), Signal-to-Noise Ratio (SNR), and Peak Signal-to-Noise Ratio (PSNR).