Boundary-Preserved Deep Denoising of Stochastic Resonance Enhanced Multiphoton Images

Objective: With the rapid growth of high-speed deep-tissue imaging in biomedical research, there is an urgent need to develop a robust and effective denoising method to retain morphological features for further texture analysis and segmentation. Conventional denoising filters and models can easily s...

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Vydané v:	IEEE journal of translational engineering in health and medicine Ročník 10; s. 1
Hlavní autori:	Niu, Sheng-Yong, Guo, Lun-Zhang, Li, Yue, Zhang, Zhiming, Wang, Tzung-Dau, Liu, Kai-Chun, Li, You-Jin, Tsao, Yu, Liu, Tzu-Ming
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Artificial neural networks Background noise Boundaries Deep Denoising Autoencoder Fluorescence Harmonic generations Image acquisition Image contrast Image enhancement Image quality Image segmentation Imaging Microprocessors Microscopy Noise reduction Optical filters Photonics Photons Signal to noise ratio Similarity Stochastic resonance Third harmonic generation Three-photon fluorescence
ISSN:	2168-2372, 2168-2372
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