An Efficient Algorithm for Speckle Noise Reduction in SAR Images Using Wavelet Transformation

Recently radar images have a large ability in different geological applications compared with the optical images. These applications are based on lineament and fault extraction, DEM generation and displacement determination. But radar images are introduced to a kind of speckle noise called speckle noise, which decreases the image quality and interpretability. Therefore, radiometric correction is an important step to increase the quality of radar images before using them.In this paper, an improved speckle noise reduction method is presented based on wavelet transform. A 2D Gaussian function is found to be the best model fitted to the speckle noise pattern cross-section in the logarithmically transformed noisy image. Therefore, a Gaussian low pass filter using a trous algorithm has been used to decompose the image. A Bayesian estimator is applied to the wavelet coefficients of the logarithmically transformed image to estimate the best value for the noise-free signal. This estimation is based on alpha-stable and Gaussian distribution hypotheses for wavelet coefficients of the signal and noise, respectively. Quantitative and qualitative comparisons of the results obtained by the new method with the results achieved from the other speckle noise reduction techniques demonstrate its higher performance for speckle reduction in SAR images.