RED: Robust Event-Guided Motion Deblurring with Modality-Specific Disentangled Representation
This work solves motion deblurring for computer vision applications, but it is incremental as it builds on existing event-guided methods.
The paper tackles motion deblurring using event cameras by addressing noise and under-reporting trade-offs, proposing a network with modality-specific disentangled representation that achieves state-of-the-art performance in accuracy and robustness on synthetic and real-world datasets.
Event cameras provide sparse yet temporally high-resolution motion information, demonstrating great potential for motion deblurring. However, the delicate events are highly susceptible to noise. Although noise can be reduced by raising the threshold of Dynamic Vision Sensors (DVS), this inevitably causes under-reporting of events. Most existing event-guided deblurring methods overlook this practical trade-off, and the indiscriminate feature extraction and naive fusion result in unstable and mixed representations and ultimately unsatisfactory performance. To tackle these challenges, we propose a Robust Event-guided Deblurring (RED) network with modality-specific disentangled representation. First, we introduce a Robustness-Oriented Perturbation Strategy (RPS) that mimics various DVS thresholds, exposing RED to diverse under-reporting patterns and thereby fostering robustness under unknown conditions. With an adaption to RPS, a Modality-specific Representation Mechanism (MRM) is designed to explicitly model semantic understanding, motion priors, and cross-modality correlations from two inherently distinct but complementary sources: blurry images and partially disrupted events. Building on these reliable features, two interactive modules are presented to enhance motion-sensitive areas in blurry images and inject semantic context into under-reporting event representations. Extensive experiments on synthetic and real-world datasets demonstrate RED consistently achieves state-of-the-art performance in terms of both accuracy and robustness.