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    Exposure trajectory recovery from motion blur

    Zhang, Y. and Wang, C. and Maybank, Stephen J. and Tao, D. (2021) Exposure trajectory recovery from motion blur. IEEE Transactions on Pattern Analysis and Machine Intelligence , ISSN 0162-8828 (print). (In Press)

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    Abstract

    Motion blur in dynamic scenes is an important yet challenging research topic. Recently, deep learning methods have achieved impressive performance for dynamic scene deblurring. However, the motion information contained in a blurry image has yet to be fully explored and accurately formulated because : (i) the ground truth of dynamic motion is difficult to obtain; (ii) the temporal ordering is destroyed during the exposure; and (iii) the motion estimation from a blurry image is highly ill-posed. By revisiting the principle of camera exposure, motion blur can be described by the relative motions of sharp content with respect to each exposed position. In this paper, we define exposure trajectories, which represent the motion information contained in a blurry image and explain the causes of motion blur. A novel motion offset estimation framework is proposed to model pixel-wise displacements of the latent sharp image at multiple timepoints. Under mild constraints, our method can recover dense, (non-)linear exposure trajectories, which significantly reduce temporal disorder and ill-posed problems. Finally, experiments demonstrate that the recovered exposure trajectories not only capture accurate and interpretable motion information from a blurry image, but also benefit motion-aware image deblurring and warping-based video extraction tasks. Codes are available on https://github.com/yjzhang96/Motion-ETR.

    Metadata

    Item Type: Article
    Additional Information: (c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
    Keyword(s) / Subject(s): Motion blur, Exposure trajectory recovery, Motion-aware image deblurring, Video extraction from a single blurry image
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Steve Maybank
    Date Deposited: 14 Oct 2021 13:42
    Last Modified: 16 Oct 2021 04:20
    URI: https://eprints.bbk.ac.uk/id/eprint/46212

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