DeMFI: Deep Joint Deblurring and Multi-Frame Interpolation with Flow-Guided Attentive Correlation and Recursive Boosting

• URL: http://arxiv.org/abs/2111.09985v1
• Date: Fri, 19 Nov 2021 00:00:15 GMT
• Title: DeMFI: Deep Joint Deblurring and Multi-Frame Interpolation with Flow-Guided Attentive Correlation and Recursive Boosting
• Authors: Jihyong Oh, Munchurl Kim
• Abstract summary: DeMFI-Net is a joint deblurring and multi-frame framework. It converts blurry videos of lower-frame-rate to sharp videos at higher-frame-rate. It achieves state-of-the-art (SOTA) performances for diverse datasets.
• Score: 50.17500790309477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a novel joint deblurring and multi-frame interpolation (DeMFI) framework, called DeMFI-Net, which accurately converts blurry videos of lower-frame-rate to sharp videos at higher-frame-rate based on flow-guided attentive-correlation-based feature bolstering (FAC-FB) module and recursive boosting (RB), in terms of multi-frame interpolation (MFI). The DeMFI-Net jointly performs deblurring and MFI where its baseline version performs feature-flow-based warping with FAC-FB module to obtain a sharp-interpolated frame as well to deblur two center-input frames. Moreover, its extended version further improves the joint task performance based on pixel-flow-based warping with GRU-based RB. Our FAC-FB module effectively gathers the distributed blurry pixel information over blurry input frames in feature-domain to improve the overall joint performances, which is computationally efficient since its attentive correlation is only focused pointwise. As a result, our DeMFI-Net achieves state-of-the-art (SOTA) performances for diverse datasets with significant margins compared to the recent SOTA methods, for both deblurring and MFI. All source codes including pretrained DeMFI-Net are publicly available at https://github.com/JihyongOh/DeMFI.

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