2 research outputs found
WhiteNNer-Blind Image Denoising via Noise Whiteness Priors
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.Comment: 9 page
Reconstructing the Noise Manifold for Image Denoising
Deep Convolutional Neural Networks (CNNs) have been successfully used in many
low-level vision problems like image denoising. Although the conditional image
generation techniques have led to large improvements in this task, there has
been little effort in providing conditional generative adversarial networks
(cGAN)[42] with an explicit way of understanding the image noise for
object-independent denoising reliable for real-world applications. The task of
leveraging structures in the target space is unstable due to the complexity of
patterns in natural scenes, so the presence of unnatural artifacts or
over-smoothed image areas cannot be avoided. To fill the gap, in this work we
introduce the idea of a cGAN which explicitly leverages structure in the image
noise space. By learning directly a low dimensional manifold of the image
noise, the generator promotes the removal from the noisy image only that
information which spans this manifold. This idea brings many advantages while
it can be appended at the end of any denoiser to significantly improve its
performance. Based on our experiments, our model substantially outperforms
existing state-of-the-art architectures, resulting in denoised images with less
oversmoothing and better detail.Comment: 18 pages, 8 figure