  With the advent of recent advances in unsupervised learning, efficient
training of a deep network for image denoising without pairs of noisy and clean
images has become feasible. However, most current unsupervised denoising
methods are built on the assumption of zero-mean noise under the
signal-independent condition. This assumption causes blind denoising techniques
to suffer brightness shifting problems on images that are greatly corrupted by
extreme noise such as salt-and-pepper noise. Moreover, most blind denoising
methods require a random masking scheme for training to ensure the invariance
of the denoising process. In this paper, we propose a dilated convolutional
network that satisfies an invariant property, allowing efficient kernel-based
training without random masking. We also propose an adaptive self-supervision
loss to circumvent the requirement of zero-mean constraint, which is
specifically effective in removing salt-and-pepper or hybrid noise where a
prior knowledge of noise statistics is not readily available. We demonstrate
the efficacy of the proposed method by comparing it with state-of-the-art
denoising methods using various examples.
