Joint Transmission and Deblurring: A Semantic Communication Approach Using Events

TL;DR

This paper introduces a joint transmission and deblurring framework using event cameras and deep learning to improve image quality over limited bandwidth, effectively handling motion blur in real-world scenarios.

Contribution

It proposes a novel JSCC system that transmits shared and domain-specific information from blurry images and events, with a multi-stage training strategy for high-quality reconstruction.

Findings

Outperforms existing JSCC schemes in handling motion blur

Effectively transmits and reconstructs clear images from blurry inputs

Addresses real-world challenges of motion blur with event-based data

Abstract

Deep learning-based joint source-channel coding (JSCC) is emerging as a promising technology for effective image transmission. However, most existing approaches focus on transmitting clear images, overlooking real-world challenges such as motion blur caused by camera shaking or fast-moving objects. Motion blur often degrades image quality, making transmission and reconstruction more challenging. Event cameras, which asynchronously record pixel intensity changes with extremely low latency, have shown great potential for motion deblurring tasks. However, the efficient transmission of the abundant data generated by event cameras remains a significant challenge. In this work, we propose a novel JSCC framework for the joint transmission of blurry images and events, aimed at achieving high-quality reconstructions under limited channel bandwidth. This approach is designed as a deblurring task-oriented JSCC system. Since RGB cameras and event cameras capture the same scene through different modalities, their outputs contain both shared and domain-specific information. To avoid repeatedly transmitting the shared information, we extract and transmit their shared information and domain-specific information, respectively. At the receiver, the received signals are processed by a deblurring decoder to generate clear images. Additionally, we introduce a multi-stage training strategy to train the proposed model. Simulation results demonstrate that our method significantly outperforms existing JSCC-based image transmission schemes, addressing motion blur effectively.