Abstract:

In order to solve the problem that many detail texture information is lost during the synthetic aperture radar (SAR) image denoising process, SAR image denoising approach based on the estimated transform domain coefficients by the means of linear minimum mean square error (LMMSE) is proposed, which combines the statistical characteristics of the speckle noise in the SAR image. Firstly, cluster image blocks into disjoint sets of similar blocks through Kmeans corresponding to the SAR scene. Secondly, perform singular value decomposition (SVD) for each set of similar blocks, and the noisy singular value coefficients containing the correlation of rows and columns of the set of similar blocks can be obtained. In order to estimate the noise-free singular value coefficients more accurately from the noisy singular value coefficients, the additive signal-dependent noise (ASDN) model is used to convert the multiplicative noise into the additive noise, then estimate the noise-free singular value coefficients by using the LMMSE technique. Finally, obtain the denoised set of similar blocks by reconstructing the estimation results. The experiment results show that the proposed method makes full use of the sparse characteristics of the set of similar blocks, and utilizes the LMMSE technique to estimate the coefficients, which can not only remove the influence of the noise but also avoid the loss of the important texture details of the image and the denoised image has better visual quality.