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

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 tiss...

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Bibliographic Details
Published in:arXiv.org 2019-04
Main Authors: Lee, Soonam, Han, Shuo, Salama, Paul, Dunn, Kenneth W, Delp, Edward J
Format: Article
Language:English
Subjects:
Blurring
Deconvolution
Fluorescence
Generative adversarial networks
Image quality
Inhomogeneity
Microscopy
Noise reduction
Quantitative analysis
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.1904.09974