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

arXiv: https://arxiv.org/abs/1904.02910 · HTML: https://mlsanity.com/paper/1904.02910/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2019.

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

- [Semi- and Self-Supervised Multi-View Fusion of 3D Microscopy Images using Generative Adversarial Networks](https://mlsanity.com/paper/2108.02743/) (2021)
- [Solving the inverse problem of microscopy deconvolution with a residual Beylkin-Coifman-Rokhlin neural network](https://mlsanity.com/paper/2407.03239/) (2024)

## Abstract (arXiv)

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.