  Speckle noise is generated due to the SAR imaging mechanism, which brings
difficulties in SAR image interpretation. Hence, despeckling is a helpful step
in SAR pre-processing. Nowadays, deep learning has been proved to be a
progressive method for SAR image despeckling. Most deep learning methods for
despeckling are based on supervised learning, which needs original SAR images
and speckle-free SAR images to train the network. However, the speckle-free SAR
images are generally not available. So, this issue was tackled by adding
multiplicative noise to optical images synthetically for simulating speckled
image. Therefore, there are following challenges in SAR image despeckling: (1)
lack of speckle-free SAR image; (2) difficulty in keeping details such as edges
and textures in heterogeneous areas. To address these issues, we propose a
self-supervised SAR despeckling strategy that can be trained without
speckle-free images. Firstly, the feasibility of SAR image despeckling without
speckle-free images is proved theoretically. Then, the sub-sampler based on the
adjacent-syntropy criteria is proposed. The training image pairs are generated
by the sub-sampler from real-word SAR image to estimate the noise distribution.
Furthermore, to make full use of training pairs, the parameter sharing
convolutional neural networks are adopted. Finally, according to the
characteristics of SAR images, a multi-feature loss function is proposed. The
proposed loss function is composed of despeckling term, regular term and
perception term, to constrain the gap between the generated paired images. The
ability of edge and texture feature preserving is improved simultaneously.
Finally, qualitative and quantitative experiments are validated on real-world
SAR images, showing better performances than several advanced SAR image
despeckling methods.
