DESPECKLING OF SYNTHETIC APERTURE RADAR IMAGES USING SHEARLET TRANSFORM

Synthetic Aperture Radar (SAR) is widely used for producing high quality imaging of Earth surface due to its capability of image acquisition in allweather conditions. However, one limitation of SAR image is that image textures and fine details are usually contaminated with multiplicative granular noise named as speckle noise. This paper presents a speckle reduction technique for SAR images based on statistical modelling of detail band shearlet coefficients (SC) in homomorphic environment. Modelling of SC corresponding to noiseless SAR image are carried out as Normal Inverse Gaussian (NIG) distribution while speckle noise SC are modelled as Gaussian distribution. These SC are segmented as heterogeneous, strongly heterogeneous and homogeneous regions depending upon the local statistics of images. Then maximum a posteriori (MAP) estimation is employed over SC that belong to homogenous and heterogenous region category. The performance of proposed method is compared with seven other methods based on objective and subjective quality measures. PSNR and SSIM metrics are used for objective assessment of synthetic images and ENL metric is used for real SAR images. Subjective assessment is carried out by visualizing denoised images obtained from various methods. The comparative result analysis shows that for the proposed method, higher values of PSNR i.e. 26.08 dB, 25.39 dB and 23.82 dB and SSIM i.e. 0.81, 0.69 and 0.61 are obtained for Barbara image at noise variances 0.04, 0.1 and 0.15, respectively as compared to other methods. For other images also results obtained for proposed method are at higher side. Also, ENL for real SAR images show highest average value of 125.91 +/- 79.05. Hence, the proposed method signifies its potential in comparison to other seven existing image denoising methods in terms of speckle de noising and edge preservation.