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Papers

Gyroscope-Aided Motion Deblurring with Deep Networks

2018-10-01 · Janne Mustaniemi, Juho Kannala, Simo Särkkä, Jiri Matas, Janne Heikkilä

We propose a deblurring method that incorporates gyroscope measurements into a convolutional neural network (CNN). With the help of such measurements, it can handle extremely strong and spatially-variant motion blur. At the same time, the image data is used to overcome the limitations of gyro-based blur estimation. To train our network, we also introduce a novel way of generating realistic training data using the gyroscope. The evaluation shows a clear improvement in visual quality over the state-of-the-art while achieving real-time performance. Furthermore, the method is shown to improve the performance of existing feature detectors and descriptors against the motion blur.

📄 PDF Abstract BibTeX arXiv:1810.00986

Code (2)

JngmnLee/DeepGyro-PyTorch_Implementation pytorch
jannemus/DeepGyro

Tasks

Deblurring

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