Neural image re-exposure

Zhang, Xinyu, Huang, Hefei, Jia, Xu, Wang, Dong, Zhang, Lihe, Zheng, Bolun, Zhou, Wei and Lu, Huchuan 2024. Neural image re-exposure. Computer Vision and Image Understanding 248 , 104094. 10.1016/j.cviu.2024.104094 Item availability restricted.

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Abstract

Images and videos often suffer from issues such as motion blur, video discontinuity, or rolling shutter artifacts. Prior studies typically focus on designing specific algorithms to address individual issues. In this paper, we highlight that these issues, albeit differently manifested, fundamentally stem from sub-optimal exposure processes. With this insight, we propose a paradigm termed re-exposure, which resolves the aforementioned issues by performing exposure simulation. Following this paradigm, we design a new architecture, which constructs visual content representation from images and event camera data, and performs exposure simulation in a controllable manner. Experiments demonstrate that, using only a single model, the proposed architecture can effectively address multiple visual issues, including motion blur, video discontinuity, and rolling shutter artifacts, even when these issues co-occur.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: Title: This article is under embargo with an end date yet to be finalised.
Publisher:	Elsevier
ISSN:	1077-3142
Date of First Compliant Deposit:	7 August 2024
Date of Acceptance:	19 July 2024
Last Modified:	08 Nov 2024 10:00
URI:	https://orca.cardiff.ac.uk/id/eprint/171046

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