Event-based video deblurring based on image and event feature fusion
- Authors
- Kim, Jeongmin; Ghosh, Dipon Kumar; Jung, Yong Ju
- Issue Date
- Aug-2023
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Keywords
- Event-based vision; Motion blur; Event-based video deblurring; Convolutional neural network
- Citation
- EXPERT SYSTEMS WITH APPLICATIONS, v.223
- Journal Title
- EXPERT SYSTEMS WITH APPLICATIONS
- Volume
- 223
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/87718
- DOI
- 10.1016/j.eswa.2023.119917
- ISSN
- 0957-4174
- Abstract
- Event-based video deblurring is a method that performs deblurring by taking the event sequence data obtained from an event camera, which is composed of bio-inspired sensors, along with blurry frames as input. Event -based video deblurring has gained attention as a method that can overcome the limitations of conventional frame-based video deblurring. In this study, we propose a novel event-based video deblurring network based on convolution neural networks (CNNs). Unlike the existing event-based deblurring methods that only use event data, the proposed method fuses all the available information from current blurry frames, previously recovered sharp frames, and event data to deblur a video. Specifically, we propose an image and event feature fusion (IEFF) module to fuse event data with current intensity frame information. Additionally, we propose a current-frame reconstruction from previous-frame (CRP) module for acquiring a pseudo sharp frame from a previously recovered sharp frame and a fusion-based residual estimation (FRE) module, which fuses the event features with the image features of the previous sharp frame extracted from the CRP module. We demonstrate through a verification experiment using synthetic and real datasets that the proposed method has superior quantitative and qualitative results compared to state-of-the-art methods.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - IT융합대학 > 소프트웨어학과 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/87718)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.