Deep-learning-based motion-correction algorithm in optical resolution photoacoustic microscopy

Deep-learning-based motion-correction algorithm in optical resolution photoacoustic microscopy

Abstract In this study, we propose a deep-learning-based method to correct motion artifacts in optical resolution photoacoustic microscopy (OR-PAM). The method is a convolutional neural network that establishes an end-to-end map from input raw data with motion artifacts to output corrected images. F...

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Bibliographic Details
Main Authors: Xingxing Chen, Weizhi Qi, Lei Xi
Format: Article
Language:English
Published: SpringerOpen 2019-10-01
Series:Visual Computing for Industry, Biomedicine, and Art
Subjects:
Deep learning
Optical resolution photoacoustic microscopy
Motion correction
Online Access:http://link.springer.com/article/10.1186/s42492-019-0022-9

Internet

http://link.springer.com/article/10.1186/s42492-019-0022-9

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