Microscopy Image Restoration with Deep Wiener-Kolmogorov filters

TL;DR

This paper introduces a deep learning-based framework for microscopy image restoration that combines Wiener-Kolmogorov filtering with learnable regularizers, improving image quality in deblurring and denoising tasks.

Contribution

It presents a unified approach integrating deep learning with classical filtering, enhancing image reconstruction and enabling effective Poisson noise handling.

Findings

Outperforms existing methods in image quality

Effective for Poisson noise deblurring with variance stabilization

Achieves competitive results with low computational complexity

Abstract

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.