  Despeckling is a key and indispensable step in SAR image preprocessing,
existing deep learning-based methods achieve SAR despeckling by learning some
mappings between speckled (different looks) and clean images. However, there
exist no clean SAR image in the real world. To this end, in this paper, we
propose a self-supervised dense dilated convolutional neural network (BDSS) for
blind SAR image despeckling. Proposed BDSS can still learn to suppress speckle
noise without clean ground truth by optimized for L2 loss. Besides, three
enhanced dense blocks with dilated convolution are employed to improve network
performance. The synthetic and real-data experiments demonstrate that proposed
BDSS can achieve despeckling effectively while maintaining well features such
as edges, point targets, and radiometric. At last, we demonstrate that our
proposed BDSS can achieve blind despeckling excellently, i.e., do not need to
care about the number of looks.
