In digital communication, efficiently transmitting image and video data through constrained channels remains challenging nowadays. Traditional methods using separate source and channel coding often fail in dynamic environments. In this paper, we introduce a novel deep learning based (DL) attention joint source channel coding (AttenJSCC) approach, which enhances robustness and efficiency in wireless image transmissions. By integrating source and channel coding into a unified framework and incorporating our Enhanced Attention Feature (EAF) modules and the ECA attention mechanism, our method outperforms some of the existing JSCC techniques, especially in low SNR conditions. Our framework not only overcomes the limitations of current technologies but also reduces the storage and computational needs on edge devices, facilitating more efficient real time communication.