# Pushing The Limits of the Wiener Filter in Image Denoising

arXiv: https://arxiv.org/abs/2303.16640 · HTML: https://mlsanity.com/paper/2303.16640/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2023.

## Built on by (most cited first, 2 of 2)

- [Lightweight Video Denoising Using a Classic Bayesian Backbone](https://mlsanity.com/paper/2408.03904/) (2024)
- [Investigating the Impact of Various Loss Functions and Learnable Wiener Filter for Laparoscopic Image Desmoking](https://mlsanity.com/paper/2509.09849/) (2025)

## Abstract (arXiv)

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.