mlsanityData to 14 Oct 2026

Learnable Residual-Based Latent Denoising in Semantic Communication

arXiv 2502.07319 ↗First posted 11 Feb 2025cs.LG cs.IT math.ITcited by 0

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Abstract (arXiv)

A latent denoising semantic communication (SemCom) framework is proposed for robust image transmission over noisy channels. By incorporating a learnable latent denoiser into the receiver, the received signals are preprocessed to effectively remove the channel noise and recover the semantic information, thereby enhancing the quality of the decoded images. Specifically, a latent denoising mapping is established by an iterative residual learning approach to improve the denoising efficiency while ensuring stable performance. Moreover, channel signal-to-noise ratio (SNR) is utilized to estimate and predict the latent similarity score (SS) for conditional denoising, where the number of denoising steps is adapted based on the predicted SS sequence, further reducing the communication latency. Finally, simulations demonstrate that the proposed framework can effectively and efficiently remove the channel noise at various levels and reconstruct visual-appealing images.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 18 references that are in the map. First posted on arXiv 11 Feb 2025.

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12 of 18 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  4. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
  5. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,358
  6. 2021Semantic Communication Systems for Speech Transmissioncited by 97
  7. 2023CDDM: Channel Denoising Diffusion Models for Wireless Semantic Communicationscited by 59
  8. 2023Generative Semantic Communication: Diffusion Models Beyond Bit Recoverycited by 41
  9. 2022WITT: A Wireless Image Transmission Transformer for Semantic Communicationscited by 37
  10. 2022Perceptual Learned Source-Channel Coding for High-Fidelity Image Semantic Transmissioncited by 12
  11. 2023Latent Semantic Diffusion-based Channel Adaptive De-Noising SemCom for Future 6G Systemscited by 11
  12. 2024Semantic Successive Refinement: A Generative AI-aided Semantic Communication Frameworkcited by 9

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