Iterative adaptive Despeckling SAR image using anisotropic diffusion filter and Bayesian estimation denoising in wavelet domain

In this paper, a new iterative algorithm has been presented by aggregating Stationary Wavelet Transform (SWT), Bilateral filtering, Bayesian estimation, and Anisotropic Diffusion (AD) filtering to reduce the speckle noise in SAR images. For this purpose, speckle images were first decomposed using two-dimensional stationary wavelet transform and then a suitable filtering method was used to filter respective coefficients of each sub-band of the speckled images. Generally, in wavelet transform-based noise reduction methods, filtering and thresholding techniques are usually applied to the coefficients of the detail sub-bands and the residual speckle noise is ignored in the approximate sub-band. In this paper, bilateral filtering has been applied to reduce the speckle noise in the approximate sub-band. We used Bayesian estimator to calculate the noise-free signal in the horizontal and vertical sub-bands with respect to that some parts of signal coefficients are eliminated in the traditional thresholding techniques. Moreover, we applied anisotropic diffusion filtering method to preserve the edges and structure of image along the diagonal subband which has more details (the entropy is maximum) than other directions in radar and optic images. Finally, both the proposed algorithm and other speckle noise reduction methods were applied on two synthetic speckled images and an actual SAR image in San Francisco. Their efficiencies were compared according to the Structural SIMilarity(SSIM), Peak Signal to Noise Ratio (PSNR), Equivalent Number of Looks (ENL), Speckle Suppression Index (SSI) and Speckle Suppression and Mean Preservation Index (SMPI). The experimental results indicate that the proposed algorithm efficiently reduces the speckle noise and preserves the edges and structure of image.