A Novel High Resolution Image Denoising Algorithm Based on Calman Filter and Texture Feature Extraction

Abstract

Calman filtering method based on wavelet transform has been successfully applied to signal denoising. According to the different application methods and the realization forms of Calman filter, combined with the structural analysis of wavelet decomposition, we present kinds of multi-scale filtering methods into the category of the three. The simulation results show that the multi-scale Calman filtering method based on system layer has better performance. Synthetic aperture radar (SAR) images have rich texture information, which can reflect the spatial structure of objects. The texture feature is widely used in SAR image classification and SAR image segmentation. Affected by imaging factors, the direct use of texture features extracted from SAR images is not good enough. In order to avoid the traditional method of filtering followed the texture feature extraction caused by the loss of texture and edge information, this paper presents a texture feature extraction of SAR image, then using Robust PCA method, finally using texture feature clustering method K-means test after treatment with RPCA expression.