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Boosting Video Super Resolution with Patch-Based Temporal Redundancy Optimization. (arXiv:2207.08674v2 [cs.CV] UPDATED)

[Submitted on 18 Jul 2022 (v1), last revised 7 Sep 2022 (this version, v2)] Download PDF Abstract: The success of existing video super-resolution (VSR) algorithms stems mainly exploiting the temporal information from the neighboring frames. However, none of these methods have discussed the influence of the temporal redundancy in the patches with stationary objects and…

[Submitted on 18 Jul 2022 (v1), last revised 7 Sep 2022 (this version, v2)]

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Abstract: The success of existing video super-resolution (VSR) algorithms stems mainly
exploiting the temporal information from the neighboring frames. However, none
of these methods have discussed the influence of the temporal redundancy in the
patches with stationary objects and background and usually use all the
information in the adjacent frames without any discrimination. In this paper,
we observe that the temporal redundancy will bring adverse effect to the
information propagation,which limits the performance of the most existing VSR
methods. Motivated by this observation, we aim to improve existing VSR
algorithms by handling the temporal redundancy patches in an optimized manner.
We develop two simple yet effective plug and play methods to improve the
performance of existing local and non-local propagation-based VSR algorithms on
widely-used public videos. For more comprehensive evaluating the robustness and
performance of existing VSR algorithms, we also collect a new dataset which
contains a variety of public videos as testing set. Extensive evaluations show
that the proposed methods can significantly improve the performance of existing
VSR methods on the collected videos from wild scenarios while maintain their
performance on existing commonly used datasets. The code is available at
this https URL.

Submission history

From: Yuhao Huang [view email]



[v1]
Mon, 18 Jul 2022 15:11:18 UTC (33,149 KB)

[v2]
Wed, 7 Sep 2022 09:11:30 UTC (33,149 KB)

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