SAR Image Despeckling Using Quadratic-Linear Approximated L1-Norm

TL;DR

This paper introduces a novel variational SAR image despeckling method that approximates the L1-norm total variation to improve accuracy and reduce computation time, validated on synthetic and real SAR images.

Contribution

It proposes a quadratic-linear approximation of the L1-norm TV regularization for faster and more accurate SAR despeckling.

Findings

Enhanced despeckling performance on SAR images.

Reduced computational time compared to traditional methods.

Effective on both synthetic and real-world SAR data.

Abstract

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.