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A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
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Exposure Trajectory Recovery From Motion Blur.

Youjian Zhang, Chaoyue Wang, Stephen J Maybank

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 28, 2021
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    Summary

    This study introduces exposure trajectories to represent motion blur in dynamic scenes. The novel framework accurately captures motion information, improving deblurring and video extraction.

    Area of Science:

    • Computer Vision
    • Image Processing

    Background:

    • Dynamic scene deblurring is challenging due to difficulties in obtaining motion ground truth, destroyed temporal order, and ill-posed motion estimation.
    • Existing deep learning methods for deblurring have not fully exploited motion information.
    • Understanding motion blur requires analyzing relative motions of sharp content during camera exposure.

    Purpose of the Study:

    • To define and utilize exposure trajectories for representing motion information in blurry images.
    • To develop a novel framework for estimating pixel-wise displacements and recovering exposure trajectories.
    • To improve dynamic scene deblurring and enable advanced video extraction techniques.

    Main Methods:

    • Defined exposure trajectories to capture motion information and explain blur formation.

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  • Proposed a novel motion offset estimation framework to model pixel-wise displacements.
  • Recovered dense, (non-)linear exposure trajectories under mild constraints.

    • Main Results:

    • The proposed method recovers accurate and interpretable motion information from blurry images.
    • Recovered exposure trajectories significantly reduce temporal disorder and ill-posed problems.
    • Demonstrated benefits for motion-aware image deblurring and warping-based video extraction.

    Conclusions:

    • Exposure trajectories offer a new way to understand and model motion blur.
    • The developed framework effectively estimates motion information from blurry images.
    • This approach advances dynamic scene deblurring and video processing.