A new SAR image despeckling using correlation based fusion and method noise thresholding

This paper presents a new technique for despeckling of Synthetic Aperture Radar (SAR) images using a local correlation based fusion of high-frequency coefficients in Discrete Wavelet Transform (DWT) with method noise thresholding. The decomposition level is decided by analyzing the texture of the input image at each level by calculating entropy. The core idea of the proposed technique lies in the selection of decomposition level in 2D-DWT based on entropy parameter and on the fusion of high-frequency coefficients. On decomposition, the low-frequency coefficients remain untouched and the high-frequency coefficients are thresholded using two different shrinkage rules. Therefore the Bayesian and Bivariate shrinkage methods are applied to the high-frequency coefficients. After performing two different thresholding methods, the improved high-frequency coefficients are fused using local correlation based strategy. The threshold value is calculated by correlation strategy. Later the correlation coefficient (CC) is evaluated between the two improved high-frequency coefficients. The CC is now compared with the threshold value for the fusion purpose. On the basis of defined fusion strategy, the average and maximum operation are applied to perform the fusion of high-frequency coefficients. The despeckling scheme is followed by method noise thresholding in order to preserve the fine details of the image. The performance of the proposed method is assessed using metrics such as Signal-to-Noise Ratio (SNR), Peak-Signal-to-Noise Ratio (PSNR), Structural Similarity Index Metric (SSIM) and visual appearance of the despeckled image. The experimental results demonstrate the effectiveness of proposed work over prior works on SAR image despeckling.