# Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer

## TL;DR

This paper introduces a novel unsupervised CNN-based method with explicit PSF layers for blind deconvolution microscopy, enhancing robustness and computational efficiency over traditional and recent CNN approaches.

## Contribution

The paper proposes a cycle consistent CNN with explicit PSF layers for blind deconvolution, improving robustness and efficiency in microscopy image restoration.

## Key findings

- The method outperforms existing approaches in robustness.
- Experimental results demonstrate high deconvolution accuracy.
- The approach is computationally efficient and effective.

## Abstract

Deconvolution microscopy has been extensively used to improve the resolution of the widefield fluorescent microscopy. Conventional approaches, which usually require the point spread function (PSF) measurement or blind estimation, are however computationally expensive. Recently, CNN based approaches have been explored as a fast and high performance alternative. In this paper, we present a novel unsupervised deep neural network for blind deconvolution based on cycle consistency and PSF modeling layers. In contrast to the recent CNN approaches for similar problem, the explicit PSF modeling layers improve the robustness of the algorithm. Experimental results confirm the efficacy of the algorithm.