Super-Resolution Reconstruction for Cross-Modal Communications in Industrial Internet of Things

Integrating visual-haptic remote control is an important application direction in the Industrial Internet of Things (IIoT). Cross-modal communications are considered to be an effective technology to support this application. However, due to limited bandwidth and competition between modalities, the quality of visual transmission and the end user's immersive experience cannot be guaranteed in practical scenarios. To overcome this dilemma, this paper proposes a super-resolution reconstruction strategy for cross-modal communications. Specifically, the sender only transmits low-resolution images and haptic signals, while a haptic-aided super-resolution reconstruction (HaSR) approach is designed at the receiver. This approach involves semantic correlation-based modal fusion and generative adversarial principle-based visual generation, which enable the reconstruction of high-resolution images using the received low-resolution images and haptic signals. Experimental results from a standard dataset and a practical remote industrial control platform validate the effectiveness of the proposed strategy.