Adaptive pseudo-p-norm regularization based De-speckling of SAR images
Yong Meng, Zeming Zhou, Yudi Liu, Qixiang Luo · Remote Sensing Letters · 2018
A novel variational model is proposed to de-speckle the multiplication noise in the synthetic aperture radar (SAR) image to provide a high-quality interpretation of SAR data. The model consists of the data fidelity term and the adaptive pseudo-p-norm regularization term. The data fidelity term ensures the convexity of the model and avoids the nonlinear image transformation. With an improved ratio of exponentially weighted averages (ROEWA) operator and the edge detection image, the exponent value of the pseudo-p-norm regularization term varies with the intensity of the edge adaptively, which results in a better retention of the image geometric structure while removing the speckle noise. Compared with the total variation (TV) regularization, the staircase-like artifacts can be restrained more effectively. Experiments demonstrate that the proposed method are more competitive both visually and quantitatively than that from the existing nonlocal mean-based and variational-based methods.