  As modern image denoiser networks have grown in size, their reported
performance in popular real noise benchmarks such as DND and SIDD have now long
outperformed classic non-deep learning denoisers such as Wiener and
Wavelet-based methods. In this paper, we propose to revisit the Wiener filter
and re-assess its potential performance. We show that carefully considering the
implementation of the Wiener filter can yield similar performance to popular
networks such as DnCNN.
