A recently proposed time-frequency filtering technique has shown promising results for the enhancement of signal-averaged electrocardiograms. This method weights the short-time Fourier transform (STFT) of the ensemble-averaged signal, analogous to the spectral domain Wiener filtering of stationary signals. In effect, it is a self-designing time-varying Wiener filter applied to the high resolution electrocardiogram (HRECG). In this paper, we empirically show that the performance of the proposed technique is about 2-3dB lower over the critical late-potential portion of the HRECG than the optimal fixed-window time-frequency filter based on ideal a priori knowledge of statistics. Although this ideal knowledge and performance is unattainable in practice, these results suggest that there remains potential for modest improvement. In order to narrow this gap in performance, we propose some improvements based on alternative structures for the time-frequency filter, including time-varying STFT windows. Simulation results show that an improved fixed-window technique can potentially yield an improvement of about 1-1.5 dB. By using properly chosen time-varying windows, the performance could potentially be improved even further. Thus, the improved techniques could produce a HRECG using fewer averages than the existing method, or could tolerate a lower signal-to-noise ratio.