The learning of blurriness representation is formulated as a ranking problem based on specially synthesized pairs. Blurriness-aware image unfolding is achieved by integrating blur relevant information contained in the representation into a base unfolding network.
Abstract— The goal of blurry image deblurring and unfolding task is to recover a single sharp frame or a sequence from a blurry one.
Oct 1, 2024 · In this work, we propose to implicitly model blur in an image by computing blurriness representation with an event-assisted blurriness encoder.
IEEE Transactions on Neural Networks and Learning Systems, 2024. 2024. Event-Assisted Blurriness Representation Learning for Blurry Image Unfolding. P Zhang, H ...
The goal of blurry image deblurring and unfolding task is to recover a single sharp frame or a sequence from a blurry one.
Aug 1, 2023 · Event-based video deblurring is a method that performs deblurring by taking the event sequence data obtained from an event camera.
Oct 14, 2024 · In this work, we propose an event-assisted blurry image unfolding framework that can work across arbitrary temporal scales.
Missing: Representation Learning
[PDF] Learning Event-Based Motion Deblurring - CVF Open Access
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Motion blur happens commonly due to the exposure time required by modern camera sensors, during which scenes are recorded at different time stamps and ...
To achieve this end, we formulate an event-enhanced degeneration model to consider the low spatial resolution, motion blurs, and event noises simultaneously. We ...
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A novel unsupervised image deblurring framework based on self-enhancement that progressively generates improved pseudo-sharp and blurry image pairs without ...