  Efficient image transmission is essential for seamless communication and
collaboration within the visually-driven digital landscape. To achieve low
latency and high-quality image reconstruction over a bandwidth-constrained
noisy wireless channel, we propose a stable diffusion (SD)-based goal-oriented
semantic communication (GSC) framework. In this framework, we design a semantic
autoencoder that effectively extracts semantic information from images to
reduce the transmission data size while ensuring high-quality reconstruction.
Recognizing the impact of wireless channel noise on semantic information
transmission, we propose an SD-based denoiser for GSC (SD-GSC) conditional on
instantaneous channel gain to remove the channel noise from the received noisy
semantic information under known channel. For scenarios with unknown channel,
we further propose a parallel SD denoiser for GSC (PSD-GSC) to jointly learn
the distribution of channel gains and denoise the received semantic
information. Experimental results show that SD-GSC outperforms state-of-the-art
ADJSCC and Latent-Diff DNSC, with the Peak Signal-to-Noise Ratio (PSNR)
improvement by 7 dB and 5 dB, and the Fr\'echet Inception Distance (FID)
reduction by 16 and 20, respectively. Additionally, PSD-GSC archives PSNR
improvement of 2 dB and FID reduction of 6 compared to MMSE equalizer-enhanced
SD-GSC.
