.png){kind=icon}
Return
Quantitative phase imaging based on model transfer learning
.png){kind=icon}
OA
AI
DOI：10.1364/OE.453112.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Convolutional neural networks have been widely used in optical information processing and the generalization ability of the network depends greatly on the scale and diversity of the datasets, however, the acquisition of mass datasets and later annotation have become a common problem that hinders its further progress. In this study, a model transfer-based quantitative phase imaging (QPI) method is proposed, which fine-tunes the network parameters through loading pre-training base model and transfer learning, enable the network with good generalization ability. Most importantly, a feature fusion method based on moment reconstruction is proposed for training dataset generation, which can construct rich enough datasets that can cover most situations and accurately annotated, it fundamentally solves the problem from the scale and representational ability of the datasets. Besides, a feature distribution distance scoring (FDDS) rule is proposed to evaluate the rationality of the constructed datasets. The experimental results show that this method is suitable for different types of samples to achieve fast and high-accuracy phase imaging, which greatly relieves the pressure of data, tagging and generalization ability in the data-driven method. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
Keywords:
PATTERN-RECOGNITION
REFRACTIVE-INDEX
ACCURATE
NETWORK
Journal
IF:
3.3
Papers:
6.1W
Citations:
14.3W
Organization
Cited Papers
ShareSaveeHoloNet: a learning-based end-to-end approach for in-line digital holographic reconstruction.png)
OPTICS EXPRESS
OAAI
OPTICS EXPRESSIF3.3
OAAIShareSaveAccurate and fast two-step phase shifting algorithm based on principle component analysis and Lissajous ellipse fitting with random phase shift and no pre-filteringOPTICS EXPRESSOAAI
OPTICS EXPRESSIF3.3
OAAIShareSaveShareSaveY-Net: a one-to-two deep learning framework for digital holographic reconstructionOPTICS LETTERS
OPTICS LETTERSIF3.3
PREAI
ShareSaveShareSaveShareSave
View more