DENOISING OF SHORT EXPOSURE TRANSMISSION ELECTRON MICROSCOPY IMAGES FOR ULTRASTRUCTURAL ENHANCEMENT

被引：0

作者：

Bajic, Buda ^{[1
]}

Suveer, Amit ^{[2
]}

Gupta, Anindya ^{[3
]}

Pepic, Ivana ^{[1
]}

Lindblad, Joakim ^{[2
,4
]}

Sladoje, Natasa ^{[2
,4
]}

Sintorn, Ida-Maria ^{[2
,5
]}

机构：

[1] Univ Novi Sad, Fac Tech Sci, Novi Sad, Serbia

[2] Uppsala Univ, Dept Informat Technol, Ctr Image Anal, Uppsala, Sweden

[3] Tallinn Univ Technol, TJ Seebeck Dept Elect, Tallinn, Estonia

[4] Serbian Acad Arts & Sci, Math Inst, Belgrade, Serbia

[5] Vironova AB, Stockholm, Sweden

来源：

2018 IEEE 15TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2018) | 2018年

基金：

瑞典研究理事会;

关键词：

Denoising; Convolutional Neural Networks; TEM; Cilia;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Transmission Electron Microscopy (TEM) is commonly used for structural analysis at the nm scale in material and biological sciences. Fast acquisition and low dose are desired to minimize the influence of external factors on the acquisition as well as the interaction of electrons with the sample. However, the resulting images are very noisy, which affects both manual and automated analysis. We present a comparative study of block matching, wavelet domain, energy minimization, and deep convolutional neural network based approaches to denoise short exposure high-resolution TEM images of cilia. In addition, we evaluate the effect of denoising before or after registering multiple short exposure images of the same imaging field to further enhance fine details.

引用

页码：921 / 925

页数：5

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