Adaptive vectorial total variation models for multi-channel synthetic aperture radar images despeckling with fast algorithms

This study proposes two adaptive vectorial total variation models for multi-channel synthetic aperture radar (SAR) images despeckling with the help of prior knowledge of the image amplitude. Besides despeckling the multi-channel SAR images efficiently, the proposed new models have advantages over other total variation methods in many aspects, such as preserving the radar reflectivity, the targets and edges contrast. The Bermudez-Moreno algorithm and the accelerated fast iterative shrinkage thresholding algorithm are employed to implement the new two models, respectively. Experimental results on multi-polarimetric, multi-temporal RADARSAT-2 images show that the visual quality and evaluation indexes of the proposed models and the corresponding algorithms outperform the other methods with edge preservation.