English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The success of the state-of-the-art video deblurring methods stems mainlyfrom implicit or explicit estimation of alignment among the adjacent frames forlatent video restoration. However, due to the influence of the blur effect,estimating the alignment information from the blurry adjacent frames is not atrivial task. Inaccurate estimations will interfere the following framerestoration. Instead of estimating alignment information, we propose a simpleand effective deep Recurrent Neural Network with Multi-scale Bi-directionalPropagation (RNN-MBP) to effectively propagate and gather the information fromunaligned neighboring frames for better video deblurring. Specifically, webuild a Multi-scale Bi-directional Propagation~(MBP) module with two U-Net RNNcells which can directly exploit the inter-frame information from unalignedneighboring hidden states by integrating them in different scales. Moreover, tobetter evaluate the proposed algorithm and existing state-of-the-art methods onreal-world blurry scenes, we also create a Real-World Blurry Video Dataset(RBVD) by a well-designed Digital Video Acquisition System (DVAS) and use it asthe training and evaluation dataset. Extensive experimental results demonstratethat the proposed RBVD dataset effectively improves the performance of existingalgorithms on real-world blurry videos, and the proposed algorithm performsfavorably against the state-of-the-art methods on three typical benchmarks. Thecode is available at https://github.com/XJTU-CVLAB-LOWLEVEL/RNN-MBP.

Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring