  Deep convolutional neural networks (CNNs) for video denoising are typically
trained with supervision, assuming the availability of clean videos. However,
in many applications, such as microscopy, noiseless videos are not available.
To address this, we propose an Unsupervised Deep Video Denoiser (UDVD), a CNN
architecture designed to be trained exclusively with noisy data. The
performance of UDVD is comparable to the supervised state-of-the-art, even when
trained only on a single short noisy video. We demonstrate the promise of our
approach in real-world imaging applications by denoising raw video,
fluorescence-microscopy and electron-microscopy data. In contrast to many
current approaches to video denoising, UDVD does not require explicit motion
compensation. This is advantageous because motion compensation is
computationally expensive, and can be unreliable when the input data are noisy.
A gradient-based analysis reveals that UDVD automatically adapts to local
motion in the input noisy videos. Thus, the network learns to perform implicit
motion compensation, even though it is only trained for denoising.
