De-speckle noise model for SAR images based on modified wavelet decomposition and partial differential equation
Ming Qiao Zhu, Xiaobo Luo · Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021) · 2022
The removal of speckle noise in synthetic aperture radar (SAR) images is important for the subsequent processing and analysis of SAR images. In order to suppress the speckle noise and improve the equivalent number of looks (ENL) in SAR images, a de-speckle noise model based on modified wavelet decomposition and partial differential equation (PDE) is proposed. In this study, the de-speckle noise model based on modified wavelet decomposition and PDE is compared with wavelet transform (WT) model, integer order total variation (TV) model, fractional order total variation (FTV) model, and tight frame (TF) model. The experimental results show that the improved total variance model has better de-noising effect on real SAR images and retains edge details, which is of practical value.