Low-Rank Representation Based Model for Sar Image Denoising and Edge-Preserving

Synthetic aperture radar imaging is a powerful remote sensing technology, but its images are often degraded by speckle noise, reducing quality. In recent years, speckle reduction has become a hot topic in SAR research, with various methods emerging. However, traditional low-rank SAR denoising causes edge blurring and loss of important features. To address this, we propose a new approach using prior knowledge to distinguish noise pixels from edges. By separating low-rank residuals and effectively extracting edge information, our method outperforms others, generating noise-suppressed SAR images while retaining features. Experiments demonstrate the effectiveness of our approach.