Joint source-channel coding schemes based on deep neural networks (DeepJSCC)
have recently achieved remarkable performance for wireless image transmission.
However, these methods usually focus only on the distortion of the
reconstructed signal at the receiver side with respect to the source at the
transmitter side, rather than the perceptual quality of the reconstruction
which carries more semantic information. As a result, severe perceptual
distortion can be introduced under extreme conditions such as low bandwidth and
low signal-to-noise ratio. In this work, we propose CommIN, which views the
recovery of high-quality source images from degraded reconstructions as an
inverse problem. To address this, CommIN combines Invertible Neural Networks
(INN) with diffusion models, aiming for superior perceptual quality. Through
experiments, we show that our CommIN significantly improves the perceptual
quality compared to DeepJSCC under extreme conditions and outperforms other
inverse problem approaches used in DeepJSCC.

在这项研究中，我们提出了 CommIN，它将从退化重构中恢复高质量源图像视为一个逆问题，并通过结合可逆神经网络（INN）和扩散模型来实现更好的感知质量。通过实验证明，在极端条件下，我们的 CommIN 在感知质量上显著提高，相比 DeepJSCC 在这方面优于其他逆问题方法。