1,255 research outputs found
Learning to Extract a Video Sequence from a Single Motion-Blurred Image
We present a method to extract a video sequence from a single motion-blurred
image. Motion-blurred images are the result of an averaging process, where
instant frames are accumulated over time during the exposure of the sensor.
Unfortunately, reversing this process is nontrivial. Firstly, averaging
destroys the temporal ordering of the frames. Secondly, the recovery of a
single frame is a blind deconvolution task, which is highly ill-posed. We
present a deep learning scheme that gradually reconstructs a temporal ordering
by sequentially extracting pairs of frames. Our main contribution is to
introduce loss functions invariant to the temporal order. This lets a neural
network choose during training what frame to output among the possible
combinations. We also address the ill-posedness of deblurring by designing a
network with a large receptive field and implemented via resampling to achieve
a higher computational efficiency. Our proposed method can successfully
retrieve sharp image sequences from a single motion blurred image and can
generalize well on synthetic and real datasets captured with different cameras
The World of Fast Moving Objects
The notion of a Fast Moving Object (FMO), i.e. an object that moves over a
distance exceeding its size within the exposure time, is introduced. FMOs may,
and typically do, rotate with high angular speed. FMOs are very common in
sports videos, but are not rare elsewhere. In a single frame, such objects are
often barely visible and appear as semi-transparent streaks.
A method for the detection and tracking of FMOs is proposed. The method
consists of three distinct algorithms, which form an efficient localization
pipeline that operates successfully in a broad range of conditions. We show
that it is possible to recover the appearance of the object and its axis of
rotation, despite its blurred appearance. The proposed method is evaluated on a
new annotated dataset. The results show that existing trackers are inadequate
for the problem of FMO localization and a new approach is required. Two
applications of localization, temporal super-resolution and highlighting, are
presented
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