论文标题
在语义通信下的图像修复
Image Restoration under Semantic Communications
论文作者
论文摘要
语义通信已通过传输和接收语义信息而不是数据位或符号无论其内容如何，作为Shannon定理之外的突破。本文提出了一个两阶段的重建过程，以提高系统的性能。在第一阶段，首先通过利用频道知识来从嘈杂接收的数据中解码图像信息。通过后过滤器和图像统计数据增强了解码的图像。利用不同的指标来评估我们所考虑的模型的图像恢复质量。使用自然图像获得数值结果，这些自然图像验证了所提出的两阶段重建过程的优越改进，而不是传统的解码数据。此外，可以根据其标准评估系统性能的不同指标可以相互冲突。
Semantic communication has emerged as the breakthrough beyond the Shannon theorem by transmitting and receiving semantic information instead of data bits or symbols regardless of its content. This paper proposes a two-stage reconstruction process to boost the system's performance. In the first phase, the image information is first decoded from the noisy received data by exploiting the channel knowledge. The decoded image is enhanced by a post-filter and image statistics. Different metrics are exploited to evaluate the image restoration quality of our considered model. Numerical results are obtained using natural images that verify the superior improvements of the proposed two-stage reconstruction process over the traditional decoded data. Moreover, the different metrics assessing the system performance based on their criteria can be conflicted with each other.
