  The accuracy of medical imaging-based diagnostics is directly impacted by the
quality of the collected images. A passive approach to improve image quality is
one that lags behind improvements in imaging hardware, awaiting better sensor
technology of acquisition devices. An alternative, active strategy is to
utilize prior knowledge of the imaging system to directly post-process and
improve the acquired images. Traditionally, priors about the image properties
are taken into account to restrict the solution space. However, few techniques
exploit the prior about the noise properties. In this paper, we propose a
neural network-based model for disentangling the signal and noise components of
an input noisy image, without the need for any ground truth training data. We
design a unified loss function that encodes priors about signal as well as
noise estimate in the form of regularization terms. Specifically, by using
total variation and piecewise constancy priors along with noise whiteness
priors such as auto-correlation and stationary losses, our network learns to
decouple an input noisy image into the underlying signal and noise components.
We compare our proposed method to Noise2Noise and Noise2Self, as well as
non-local mean and BM3D, on three public confocal laser endomicroscopy
datasets. Experimental results demonstrate the superiority of our network
compared to state-of-the-art in terms of PSNR and SSIM.
