Video Deblurring with Deconvolution and Aggregation Networks

TL;DR

This paper introduces a novel video deblurring network that effectively utilizes neighboring frames through specialized sub-networks, achieving superior performance over existing methods.

Contribution

The paper proposes a deconvolution and aggregation network with three sub-networks that better exploit neighbor frames for improved video deblurring.

Findings

DAN outperforms state-of-the-art methods on public datasets.

The network effectively combines deconvolution and aggregation strategies.

Experimental results show significant qualitative and quantitative improvements.

Abstract

In contrast to single-image deblurring, video deblurring has the advantage that neighbor frames can be utilized to deblur a target frame. However, existing video deblurring algorithms often fail to properly employ the neighbor frames, resulting in sub-optimal performance. In this paper, we propose a deconvolution and aggregation network (DAN) for video deblurring that utilizes the information of neighbor frames well. In DAN, both deconvolution and aggregation strategies are achieved through three sub-networks: the preprocessing network (PPN) and the alignment-based deconvolution network (ABDN) for the deconvolution scheme; the frame aggregation network (FAN) for the aggregation scheme. In the deconvolution part, blurry inputs are first preprocessed by the PPN with non-local operations. Then, the output frames from the PPN are deblurred by the ABDN based on the frame alignment. In the FAN, these deblurred frames from the deconvolution part are combined into a latent frame according to reliability maps which infer pixel-wise sharpness. The proper combination of three sub-networks can achieve favorable performance on video deblurring by using the neighbor frames suitably. In experiments, the proposed DAN was demonstrated to be superior to existing state-of-the-art methods through both quantitative and qualitative evaluations on the public datasets.