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Learn to Model Motion from Blurry Footages
[article]
2017
arXiv
pre-print
It is difficult to recover the motion field from a real-world footage given a mixture of camera shake and other photometric effects. In this paper we propose a hybrid framework by interleaving a Convolutional Neural Network (CNN) and a traditional optical flow energy. We first conduct a CNN architecture using a novel learnable directional filtering layer. Such layer encodes the angle and distance similarity matrix between blur and camera motion, which is able to enhance the blur features of the
arXiv:1704.05817v1
fatcat:idmih7ssr5aqflt26rhdnxm7xa