GPU-based deep convolutional neural network for tomographic phase microscopy with â1 fitting and regularization.
J Biomed Opt
; 23(6): 1-7, 2018 06.
Article
em En
| MEDLINE
| ID: mdl-29905037
ABSTRACT
Tomographic phase microscopy (TPM) is a unique imaging modality to measure the three-dimensional refractive index distribution of transparent and semitransparent samples. However, the requirement of the dense sampling in a large range of incident angles restricts its temporal resolution and prevents its application in dynamic scenes. Here, we propose a graphics processing unit-based implementation of a deep convolutional neural network to improve the performance of phase tomography, especially with much fewer incident angles. As a loss function for the regularized TPM, the â1-norm sparsity constraint is introduced for both data-fidelity term and gradient-domain regularizer in the multislice beam propagation model. We compare our method with several state-of-the-art algorithms and obtain at least 14 dB improvement in signal-to-noise ratio. Experimental results on HeLa cells are also shown with different levels of data reduction.
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MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
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Células HeLa
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Microscopia de Contraste de Fase
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Redes Neurais de Computação
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article