Pixel-Wise Motion Deblurring of Thermal Videos
- URL: http://arxiv.org/abs/2006.04973v1
- Date: Mon, 8 Jun 2020 22:35:12 GMT
- Title: Pixel-Wise Motion Deblurring of Thermal Videos
- Authors: Manikandasriram Srinivasan Ramanagopal, Zixu Zhang, Ram Vasudevan,
Matthew Johnson-Roberson
- Abstract summary: Uncooled microbolometers can enable robots to see in the absence of visible illumination by imaging the "heat" radiated from the scene.
Despite this ability to see in the dark, these sensors suffer from significant motion blur.
This paper formulates reversing the effect of thermal inertia at a single pixel as a Least Absolute Shrinkage and Selection Operator (LASSO) problem.
- Score: 26.6875886332029
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Uncooled microbolometers can enable robots to see in the absence of visible
illumination by imaging the "heat" radiated from the scene. Despite this
ability to see in the dark, these sensors suffer from significant motion blur.
This has limited their application on robotic systems. As described in this
paper, this motion blur arises due to the thermal inertia of each pixel. This
has meant that traditional motion deblurring techniques, which rely on
identifying an appropriate spatial blur kernel to perform spatial
deconvolution, are unable to reliably perform motion deblurring on thermal
camera images. To address this problem, this paper formulates reversing the
effect of thermal inertia at a single pixel as a Least Absolute Shrinkage and
Selection Operator (LASSO) problem which we can solve rapidly using a quadratic
programming solver. By leveraging sparsity and a high frame rate, this
pixel-wise LASSO formulation is able to recover motion deblurred frames of
thermal videos without using any spatial information. To compare its quality
against state-of-the-art visible camera based deblurring methods, this paper
evaluated the performance of a family of pre-trained object detectors on a set
of images restored by different deblurring algorithms. All evaluated object
detectors performed systematically better on images restored by the proposed
algorithm rather than any other tested, state-of-the-art methods.
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