Speckle noise reduction in images using Wiener filtering and adaptive Wavelet thresholding

Synthetic Aperture Radar (SAR) is a coherent microwave imaging method used for mapping scattering properties of Earth's surface. Speckle is a granular disturbance, modeled as a multiplicative noise, that affects all types of coherent images. Most of the applications demand speckle reduction to be carried out in real-time and require computationally simple and efficient techniques. The paper presents a combination of spatial and frequency domain techniques for removing speckle noise. The algorithm uses a Wiener filter as a preprocessing stage in the spatial domain and adaptive soft thresholding of Wavelet transform coefficients in the frequency domain. Considering multiplicative model for speckle noise, logarithmic transform is applied to Wiener filtered image. Threshold value at each decomposition level and subband is computed from the corresponding standard deviation. Simulation results show that the proposed method is efficient in removing speckle noise compared to an existing algorithm. The results are quantitatively analysed using Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM) metrics.