131,827 research outputs found
Phase Retrieval for Sparse Signals
The aim of this paper is to build up the theoretical framework for the
recovery of sparse signals from the magnitude of the measurement. We first
investigate the minimal number of measurements for the success of the recovery
of sparse signals without the phase information. We completely settle the
minimality question for the real case and give a lower bound for the complex
case. We then study the recovery performance of the minimization. In
particular, we present the null space property which, to our knowledge, is the
first sufficient and necessary condition for the success of
minimization for -sparse phase retrievable.Comment: 14 page
Occlusion Aware Unsupervised Learning of Optical Flow
It has been recently shown that a convolutional neural network can learn
optical flow estimation with unsupervised learning. However, the performance of
the unsupervised methods still has a relatively large gap compared to its
supervised counterpart. Occlusion and large motion are some of the major
factors that limit the current unsupervised learning of optical flow methods.
In this work we introduce a new method which models occlusion explicitly and a
new warping way that facilitates the learning of large motion. Our method shows
promising results on Flying Chairs, MPI-Sintel and KITTI benchmark datasets.
Especially on KITTI dataset where abundant unlabeled samples exist, our
unsupervised method outperforms its counterpart trained with supervised
learning.Comment: CVPR 2018 Camera-read
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