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Learning to Deblur and Generate High Frame Rate Video with an Event Camera

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arxiv 2003.00847 v2 pith:TQD2NVNU submitted 2020-03-02 cs.CV

Chen Haoyu , Teng Minggui , Shi Boxin , Wang YIzhou , Huang Tiejun This is my paper · ORCID
classification cs.CV
keywords camerashigheventeventsframeimagelearningnetwork
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Event cameras are bio-inspired cameras which can measure the change of intensity asynchronously with high temporal resolution. One of the event cameras' advantages is that they do not suffer from motion blur when recording high-speed scenes. In this paper, we formulate the deblurring task on traditional cameras directed by events to be a residual learning one, and we propose corresponding network architectures for effective learning of deblurring and high frame rate video generation tasks. We first train a modified U-Net network to restore a sharp image from a blurry image using corresponding events. Then we train another similar network with different downsampling blocks to generate high frame rate video using the restored sharp image and events. Experiment results show that our method can restore sharper images and videos than state-of-the-art methods.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 23 citations worldwide. Full citation record

  1. Weaving Light and Time: Unified Harmonic-Geometric Representation Learning for Dense RGB-Event Parsing

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Evita, a unified RGB-Event backbone with geometric rectification, spectral resonance, and transient routing, plus N-ImageNetV2 pretraining, reports SOTA dense parsing with better accuracy-latency trade-offs.

  2. High-Speed Dynamic 3D Imaging with Sensor Fusion Splatting

    cs.CV 2025-02 conditional novelty 5.0 of 10

    A sensor fusion method that jointly optimizes deformable 3D Gaussians against RGB, event, and depth data for high-speed dynamic 3D reconstruction.

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