  We devise a new regularization, called self-verification, for image
denoising. This regularization is formulated using a deep image prior learned
by the network, rather than a traditional predefined prior. Specifically, we
treat the output of the network as a ``prior'' that we denoise again after
``re-noising''. The comparison between the again denoised image and its prior
can be interpreted as a self-verification of the network's denoising ability.
We demonstrate that self-verification encourages the network to capture
low-level image statistics needed to restore the image. Based on this
self-verification regularization, we further show that the network can learn to
denoise even if it has not seen any clean images. This learning strategy is
self-supervised, and we refer to it as Self-Verification Image Denoising
(SVID). SVID can be seen as a mixture of learning-based methods and traditional
model-based denoising methods, in which regularization is adaptively formulated
using the output of the network. We show the application of SVID to various
denoising tasks using only observed corrupted data. It can achieve the
denoising performance close to supervised CNNs.
