Event cameras, which detect local brightness changes instead of capturing full-frame images, offer high temporal resolution and low latency. Although existing convolutional neural networks (CNNs) and transformer-based methods for event-based video reconstruction have achieved impressive results, they suffer from high computational costs due to their linear operations. These methods often require 10M-30M parameters and inference times of 30-110 ms per forward pass at a resolution of 640 × 480 on modern GPUs. Furthermore, to reduce computational costs, these methods apply CNN-based downsampling, which leads to the loss of fine details. To address these challenges, we propose an efficient hybrid model, WaveE2VID, which combines the frequency-domain analysis of the wavelet transform with the spatio-temporal context modeling of a deep convolutional recurrent network. Our model achieves 50% faster inference speed and lower GPU memory usage than CNN and transformer-based methods, maintaining reconstruction performance on par with state-of-the-art approaches across benchmark datasets.

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Event camera,Wavelet transform,Deep learning,Video reconstruction

• Computer and Information Sciences - Natural Sciences
• Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology

This publication is associated with the following record:

• WaveE2VID: Frequency-Aware Event-Based Video Reconstruction