Human and Scene Motion Deblurring using Pseudo-blur Synthesizer
- URL: http://arxiv.org/abs/2111.12911v1
- Date: Thu, 25 Nov 2021 04:56:13 GMT
- Title: Human and Scene Motion Deblurring using Pseudo-blur Synthesizer
- Authors: Jonathan Samuel Lumentut, In Kyu Park
- Abstract summary: Present-day deep learning-based motion deblurring methods utilize the pair of synthetic blur and sharp data to regress any particular framework.
We provide an on-the-fly blurry data augmenter that can be run during training and test stages.
The proposed module is also equipped with hand-crafted prior extracted using the state-of-the-art human body statistical model.
- Score: 17.36135319921425
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Present-day deep learning-based motion deblurring methods utilize the pair of
synthetic blur and sharp data to regress any particular framework. This task is
designed for directly translating a blurry image input into its restored
version as output. The aforementioned approach relies heavily on the quality of
the synthetic blurry data, which are only available before the training stage.
Handling this issue by providing a large amount of data is expensive for common
usage. We answer this challenge by providing an on-the-fly blurry data
augmenter that can be run during training and test stages. To fully utilize it,
we incorporate an unorthodox scheme of deblurring framework that employs the
sequence of blur-deblur-reblur-deblur steps. The reblur step is assisted by a
reblurring module (synthesizer) that provides the reblurred version
(pseudo-blur) of its sharp or deblurred counterpart. The proposed module is
also equipped with hand-crafted prior extracted using the state-of-the-art
human body statistical model. This prior is employed to map human and non-human
regions during adversarial learning to fully perceive the characteristics of
human-articulated and scene motion blurs. By engaging this approach, our
deblurring module becomes adaptive and achieves superior outcomes compared to
recent state-of-the-art deblurring algorithms.
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