  In this letter, we aim to address a synthetic aperture radar (SAR)
despeckling problem with the necessity of neither clean (speckle-free) SAR
images nor independent speckled image pairs from the same scene, and a
practical solution for SAR despeckling (PSD) is proposed. First, an adversarial
learning framework is designed to generate speckled-to-speckled (S2S) image
pairs from the same scene in the situation where only single speckled SAR
images are available. Then, the S2S SAR image pairs are employed to train a
modified despeckling Nested-UNet model using the Noise2Noise (N2N) strategy.
Moreover, an iterative version of the PSD method (PSDi) is also presented.
Experiments are conducted on both synthetic speckled and real SAR data to
demonstrate the superiority of the proposed methods compared with several
state-of-the-art methods. The results show that our methods can reach a good
tradeoff between feature preservation and speckle suppression.
