Self-supervised Exposure Trajectory Recovery for Dynamic Blur Estimation
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Dynamic scene blurring is an important yet challenging 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 blurry motion is difficult to obtain; (ii) the temporal ordering is destroyed during the exposure; and (iii) the motion estimation is highly ill-posed. By revisiting the principle of camera exposure, dynamic blur can be described by the relative motions of sharp content with respect to each exposed pixel. We define exposure trajectories, which record the trajectories of relative motions to represent the motion information contained in a blurry image and explain the causes of the dynamic blur. A new blur representation, which we call motion offset, is proposed to model pixel-wise displacements of the latent sharp image at multiple timepoints. Under mild constraints, the learned motion offsets can recover dense, (non-)linear exposure trajectories, which significantly reduce temporal disorder and ill-posed problems. Finally, we demonstrate that the estimated exposure trajectories can fit real-world dynamic blurs and further contribute to motion-aware image deblurring and warping-based video extraction from a single blurry image.
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