The standard 12-lead ECG can represent the cardiac status from twelve different perspectives, while the single-lead ECG just involves a single view, so it is challenging for the single-lead ECG to provide information as rich as the 12-lead ECG. Therefore, this study adopts the improved generative adversarial network (GAN) framework to model the potential mapping relation between single-lead and 12-lead ECG. Taking a heartbeat (1024 points) in lead I as input, in which the R-peak aligning strategy will be used for variable-duration reconstruction. The average correlation coefficients(CC) between the generated and the real signals are 0.742 at the heartbeat level and 0.744 at the segment level, and the CC in lead aVR could be up to 0.91 at the segment level. This study demonstrates that the generated 12-lead ECG signals effectively retain pathological features, thereby providing valuable multi-scale diagnostic information. To assess the efficacy of the diagnostic information derived from the generated signals, this study employs the arrhythmia classifiers for both single-lead and 12-lead ECG, achieving accuracy of 0.71 and 0.81, and macro-F1 scores of 0.66 and 0.77. For the generated 12-lead ECG, the accuracy and macro-F1 for arrhythmia classification could be 0.74 and 0.70, successfully bridging the gap between the single-lead ECG and the 12-lead ECG. In addition, the supplement experiments for other cases have been shown and compared. The source code is available at https://github.com/Zehui-Zhan/12-lead-reconstruction.git.