ClearSight: Human Vision-Inspired Solutions for Event-Based Motion Deblurring
This work addresses motion blur in imaging for applications like robotics and autonomous systems, presenting an incremental improvement through a novel hybrid network design.
The paper tackles motion deblurring in images using event cameras by proposing a bioinspired dual-drive hybrid network (BDHNet) that integrates Spiking Neural Networks for motion features and Artificial Neural Networks for color processing, achieving state-of-the-art performance on synthetic and real-world datasets.
Motion deblurring addresses the challenge of image blur caused by camera or scene movement. Event cameras provide motion information that is encoded in the asynchronous event streams. To efficiently leverage the temporal information of event streams, we employ Spiking Neural Networks (SNNs) for motion feature extraction and Artificial Neural Networks (ANNs) for color information processing. Due to the non-uniform distribution and inherent redundancy of event data, existing cross-modal feature fusion methods exhibit certain limitations. Inspired by the visual attention mechanism in the human visual system, this study introduces a bioinspired dual-drive hybrid network (BDHNet). Specifically, the Neuron Configurator Module (NCM) is designed to dynamically adjusts neuron configurations based on cross-modal features, thereby focusing the spikes in blurry regions and adapting to varying blurry scenarios dynamically. Additionally, the Region of Blurry Attention Module (RBAM) is introduced to generate a blurry mask in an unsupervised manner, effectively extracting motion clues from the event features and guiding more accurate cross-modal feature fusion. Extensive subjective and objective evaluations demonstrate that our method outperforms current state-of-the-art methods on both synthetic and real-world datasets.