SAR Image Despeckling Using Quadratic-Linear Approximated L1-Norm
This addresses the problem of degraded SAR image analysis for remote sensing applications, but it is incremental as it builds on existing variational methods with a specific approximation.
The paper tackled speckle noise reduction in SAR images by proposing a variational despeckling approach with a quadratic-linear approximated L1-norm total variation regularization, resulting in increased accuracy and decreased computation time as demonstrated on synthetic and real-world images.
Speckle noise, inherent in synthetic aperture radar (SAR) images, degrades the performance of the various SAR image analysis tasks. Thus, speckle noise reduction is a critical preprocessing step for smoothing homogeneous regions while preserving details. This letter proposes a variational despeckling approach where L1-norm total variation regularization term is approximated in a quadratic and linear manner to increase accuracy while decreasing the computation time. Despeckling performance and computational efficiency of the proposed method are shown using synthetic and real-world SAR images.