Denoising is the task of removing noise from an image.
This paper proposes an upgraded electro-magnetic side-channel attack that automatically reconstructs the intercepted data.
We cast the problem of image denoising as a domain translation problem between high and low noise domains.
To improve the compressive sensing MRI (CS-MRI) approaches in terms of fine structure loss under high acceleration factors, we have proposed an iterative feature refinement model (IFR-CS), equipped with fixed transforms, to restore the meaningful structures and details.
Distant supervision for relation extraction enables one to effectively acquire structured relations out of very large text corpora with less human efforts.
Specifically, the proposed autoencoder exploits multiple views of the same scene from different points of view in order to learn to suppress noise in a self-supervised end-to-end manner using depth and color information during training, yet only depth during inference.
At the stage of prior learning, transformed feature images obtained by undecimated wavelet transform are stacked as an input of denoising autoencoder network (DAE).
On one hand, as other data-driven deep learning methods, our method, namely variational denoising network (VDN), can perform denoising efficiently due to its explicit form of posterior expression.