AI System Recovers Clearer Images From Degraded Wireless Signals
Researchers have developed a machine-learning approach that reconstructs images transmitted across multiple unreliable network hops with significantly better visual quality than existing methods. The breakthrough could improve wireless imaging in remote sensing, autonomous vehicles, and secure communications where traditional compression fails under poor signal conditions.
Originaltitel: Multi-Hop Deep Joint Source-Channel Coding With Deep Hash Distillation for Semantically Aligned Image Recovery
We consider image transmission via deep joint source-channel coding (DeepJSCC) over multi-hop additive white Gaussian noise (AWGN) channels by training a DeepJSCC encoder-decoder pair with a pre-trained deep hash distillation (DHD) module to semantically cluster images, facilitating security-oriented applications through enhanced semantic consistency and improving the perceptual reconstruction quality. We train the DeepJSCC module to both reduce mean squared error (MSE) and minimize cosine distance between DHD hashes of source and reconstructed images. Significantly improved perceptual quality as a result of semantic alignment is illustrated for different multi-hop settings, for which classical DeepJSCC may suffer from noise accumulation, measured by the learned perceptual image patch similarity (LPIPS) metric.