  Mechanical image stabilization using actuated gimbals enables capturing
long-exposure shots without suffering from blur due to camera motion. These
devices, however, are often physically cumbersome and expensive, limiting their
widespread use. In this work, we propose to digitally emulate a mechanically
stabilized system from the input of a fast unstabilized camera. To exploit the
trade-off between motion blur at long exposures and low SNR at short exposures,
we train a CNN that estimates a sharp high-SNR image by aggregating a burst of
noisy short-exposure frames, related by unknown motion. We further suggest
learning the burst's exposure times in an end-to-end manner, thus balancing the
noise and blur across the frames. We demonstrate this method's advantage over
the traditional approach of deblurring a single image or denoising a
fixed-exposure burst on both synthetic and real data.
