A Speckle Reduction Model for Sar Images Based on Beltrami Regularization
Yong Meng, Yudi Liu, Zeming Zhou, Qixiang Luo · 2018
A novel variational model is proposed to remove the multiplication noise in the synthetic aperture radar (SAR) images to provide a high quality of interpretation of SAR data. The model consists of the data fidelity term and Beltrami regularization term. The former ensures the convexity of the model and avoids the nonlinear image transformation. The latter restrains the staircase-like artifacts caused by total variation (TV) regularization and preserves the geometric structure of the image effectively. Experiments on both synthetic and real SAR data demonstrate that, compared with three state-of-the-art methods, the proposed method achieves competitive results both visually and quantitatively.