# Three dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networks

## TL;DR

This paper introduces a 3D blind image deconvolution technique using cycle-consistent adversarial networks to enhance fluorescence microscopy images, especially at deeper tissue depths, by reducing blur and noise.

## Contribution

It proposes a novel 3D deep learning-based deconvolution method that outperforms existing techniques in restoring blurred and noisy microscopy images.

## Key findings

- Improved image quality in deep tissue fluorescence microscopy.
- Quantitative and visual validation shows superior performance.
- Effective reduction of blur and noise in 3D microscopy volumes.

## Abstract

Due to image blurring image deconvolution is often used for studying biological structures in fluorescence microscopy. Fluorescence microscopy image volumes inherently suffer from intensity inhomogeneity, blur, and are corrupted by various types of noise which exacerbate image quality at deeper tissue depth. Therefore, quantitative analysis of fluorescence microscopy in deeper tissue still remains a challenge. This paper presents a three dimensional blind image deconvolution method for fluorescence microscopy using 3-way spatially constrained cycle-consistent adversarial networks. The restored volumes of the proposed deconvolution method and other well-known deconvolution methods, denoising methods, and an inhomogeneity correction method are visually and numerically evaluated. Experimental results indicate that the proposed method can restore and improve the quality of blurred and noisy deep depth microscopy image visually and quantitatively.