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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 thereconstructed signal at the receiver side with respect to the source at thetransmitter side, rather than the perceptual quality of the reconstructionwhich carries more semantic information. As a result, severe perceptualdistortion can be introduced under extreme conditions such as low bandwidth andlow signal-to-noise ratio. In this work, we propose CommIN, which views therecovery of high-quality source images from degraded reconstructions as aninverse problem. To address this, CommIN combines Invertible Neural Networks(INN) with diffusion models, aiming for superior perceptual quality. Throughexperiments, we show that our CommIN significantly improves the perceptualquality compared to DeepJSCC under extreme conditions and outperforms otherinverse problem approaches used in DeepJSCC.

CommIN: Semantic Image Communications as an Inverse Problem with INN-Guided Diffusion Models