Edge-aware nonlinear diffusion-driven regularization model for despeckling synthetic aperture radar images

Speckle noise corrupts synthetic aperture radar (SAR) images and limits their applications in sensitive scientific and engineering fields. This challenge has attracted several scholars because of the wide demand of SAR images in forestry, oceanography, geology, glaciology, and topography. Despite some significant efforts to address the challenge, an open-ended research question remains to simultaneously suppress speckle noise and to restore semantic features in SAR images. Therefore, this work establishes a diffusion-driven nonlinear method with edge-awareness capabilities to restore corrupted SAR images while protecting critical image features, such as contours and textures. The proposed method incorporates two terms that promote effective noise removal: (1) high-order diffusion kernel; and (2) fractional regularization term that is sensitive to speckle noise. These terms have been carefully designed to ensure that the restored SAR images contain stronger edges and well-preserved textures. Empirical results show that the proposed model produces content-rich images with higher subjective and objective values. Furthermore, our model generates images with unnoticeable staircase and block artifacts, which are commonly found in the classical Perona–Malik and Total variation models.