A new deep learning method for image deblurring in optical microscopic systems:
Deconvolution is the most commonly used image processing method in optical imaging systems to remove the blur caused by the point‐spread‐function (PSF). While this method has been successful in deblurring, it suffers from several disadvantages, such as slow processing time due to multiple iterations required to deblur and suboptimal in cases where the experimental operator chosen to represent PSF is not optimal. In this paper, we present a deep‐learning‐based deblurring method which is fast and applicable to optical microscopic imaging systems. We tested the robustness of proposed deblurring method on the publicly available data, simulated data, and experimental data (including 2D optical microscopic data and 3D photoacoustic microscopic data), which all showed much improved deblurred results compared to deconvolution. We compared our results against several existing deconvolution methods. Our results are better than conventional techniques and do not require multiple iterations or pre‐determined experimental operator. Our method has several advantages including simple operation, short time to compute, good deblur results and wide application in all types of optical microscopic imaging systems. The deep learning approach opens up a new path for deblurring and can be applied in various biomedical imaging fields.
This article is protected by copyright. All rights reserved.
Medicine by Alexandros G. Sfakianakis,Anapafseos 5 Agios Nikolaos 72100 Crete Greece,00302841026182,00306932607174,alsfakia@gmail.com,
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Παρασκευή 20 Δεκεμβρίου 2019
A new deep learning method for image deblurring in optical microscopic systems
Αναρτήθηκε από
Medicine by Alexandros G. Sfakianakis,Anapafseos 5 Agios Nikolaos 72100 Crete Greece,00302841026182,00306932607174,alsfakia@gmail.com,
στις
12:02 π.μ.
Ετικέτες
00302841026182,
00306932607174,
alsfakia@gmail.com,
Anapafseos 5 Agios Nikolaos 72100 Crete Greece,
Medicine by Alexandros G. Sfakianakis,
Telephone consultation 11855 int 1193
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