# Microscopy Image Restoration with Deep Wiener-Kolmogorov filters

arXiv: https://arxiv.org/abs/1911.10989 · HTML: https://mlsanity.com/paper/1911.10989/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 7 of 7)

- [Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imaging](https://mlsanity.com/paper/2203.05569/) (2022)
- [Feather-Light Fourier Domain Adaptation in Magnetic Resonance Imaging](https://mlsanity.com/paper/2208.00474/) (2022)
- [Clean Implicit 3D Structure from Noisy 2D STEM Images](https://mlsanity.com/paper/2203.15434/) (2022)
- [F2former: When Fractional Fourier Meets Deep Wiener Deconvolution and Selective Frequency Transformer for Image Deblurring](https://mlsanity.com/paper/2409.02056/) (2024)
- [Differentiable Microscopy for Content and Task Aware Compressive Fluorescence Imaging](https://mlsanity.com/paper/2203.14945/) (2022)
- [Self-supervised Physics-based Denoising for Computed Tomography](https://mlsanity.com/paper/2211.00745/) (2022)
- [Aberration Correcting Vision Transformers for High-Fidelity Metalens Imaging](https://mlsanity.com/paper/2412.04591/) (2024)

## Abstract (arXiv)

Microscopy is a powerful visualization tool in biology, enabling the study of cells, tissues, and the fundamental biological processes; yet, the observed images typically suffer from blur and background noise. In this work, we propose a unifying framework of algorithms for Gaussian image deblurring and denoising. These algorithms are based on deep learning techniques for the design of learnable regularizers integrated into the Wiener-Kolmogorov filter. Our extensive experimentation line showcases that the proposed approach achieves a superior quality of image reconstruction and surpasses the solutions that rely either on deep learning or on optimization schemes alone. Augmented with the variance stabilizing transformation, the proposed reconstruction pipeline can also be successfully applied to the problem of Poisson image deblurring, surpassing the state-of-the-art methods. Moreover, several variants of the proposed framework demonstrate competitive performance at low computational complexity, which is of high importance for real-time imaging applications.