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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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Related Experiment Video

Updated: Sep 20, 2025

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
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Published on: February 23, 2024

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MoBluRF: Motion Deblurring Neural Radiance Fields for Blurry Monocular Video.

Minh-Quan Viet Bui, Jongmin Park, Jihyong Oh

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 28, 2025
    PubMed
    Summary

    This study introduces MoBluRF, a novel framework for deblurring videos using Neural Radiance Fields (NeRF). MoBluRF effectively synthesizes sharp views from blurry monocular videos by decomposing motion, outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Machine Learning

    Background:

    • Neural Radiance Fields (NeRF) excel at novel view synthesis for static scenes.
    • Motion blur in videos, caused by movement during exposure, poses a significant challenge for synthesizing sharp spatio-temporal views.
    • Existing NeRF-based video synthesis methods struggle with motion blur, limiting their effectiveness.

    Purpose of the Study:

    • To propose a novel motion deblurring NeRF framework, MoBluRF, specifically designed for blurry monocular videos.
    • To address the challenge of synthesizing sharp spatio-temporal views from videos affected by motion blur.
    • To develop a method that can effectively decompose and handle both global camera motion and local object motion.

    Main Methods:

    • Introduced MoBluRF, a framework with two stages: Base Ray Initialization (BRI) and Motion Decomposition-based Deblurring (MDD).
    • The BRI stage coarsely reconstructs dynamic 3D scenes and initializes base rays for predicting latent sharp rays using inaccurate camera poses.
    • The MDD stage employs Incremental Latent Sharp-rays Prediction (ILSP) to decompose latent sharp rays into global and local motion components, utilizing novel loss functions for geometry regularization and scene decomposition without masks.

    Main Results:

    • MoBluRF successfully synthesizes sharp spatio-temporal views from blurry monocular videos.
    • The proposed method effectively decomposes latent sharp rays into global camera motion and local object motion.
    • Experiments demonstrate that MoBluRF significantly outperforms state-of-the-art methods both qualitatively and quantitatively.

    Conclusions:

    • MoBluRF presents a robust solution for motion deblurring in monocular videos using a NeRF-based approach.
    • The framework's ability to decompose motion and regularize geometry enables high-quality view synthesis.
    • MoBluRF advances the field of video novel view synthesis by effectively handling motion blur.