DIFFSC: Semantic Communication Framework With Enhanced Denoising Through Diffusion Probabilistic Models
Zeyu Jiang, Xiaohong Liu, Guoxing Yang, Weizhi Li, Aini Li, Guangyu Wang
Abstract
In communication systems, the challenge of ensuring accurate data transmission across noisy channels remains paramount. While semantic communication shows potential in improving image transmission and reconstruction, existing methods still suffer from perceptual quality degradation in high-noise environments. To address these issues, we introduce DiffSC, a novel semantic communication framework that integrates the Diffusion Probabilistic Model (DPM). Within DPM, Gaussian noise modeling is leveraged to facilitate enhanced image generation. DiffSC is trained to recover semantic information compromised during transmission, amplifying its capabilities in both image reconstruction and denoising. Additionally, we propose a Multi-Dimensional Feature Extraction Module (MFM) that employs multiple convolution kernels along with channel and spatial attention mechanisms to enrich encoding and decoding. Experimental results demonstrate that DiffSC significantly outperforms existing systems, improving SSIM by more than 19% and PSNR by more than 11% compared to Deep JSCC. Further ablation studies demonstrate the effectiveness of our proposed denoiser and feature extraction modules.