@misc{Jin_Shuyang_Phase_2023,
 author={Jin, Shuyang and Xu, Xiaoqing and Chen, Jili and Ni, Yudan},
 contributor={Urbańczyk, Wacław. Redakcja},
 identifier={DOI: 10.37190/oa230109},
 year={2023},
 rights={Wszystkie prawa zastrzeżone (Copyright)},
 publisher={Oficyna Wydawnicza Politechniki Wrocławskiej},
 description={Optica Applicata, Vol. 53, 2023, nr 1, s. 127-140},
 description={Optica Applicata is an international journal, published in a non-periodical form in the years 1971-1973 and quarterly since 1973. From the beginning of the year 2008, Optica Applicata is an Open Access journal available online via the Internet, with free access to the full text of articles serving the best interests of the scientific community. The journal is abstracted and indexed in: Chemical Abstracts, Compendex, Current Contents, Inspec, Referativnyj Zhurnal, SCI Expanded, Scopus, Ulrich’s Periodicals Directory},
 description={http://opticaapplicata.pwr.edu.pl/},
 language={eng},
 abstract={Phase retrieval and phase unwrapping are the two important problems for enabling quantitative phase imaging of cells in phase-shifting digital holography. To simultaneously cope with these two problems, a deep-learning phase-shifting digital holography method is proposed in this paper. The proposed method can establish the continuous mapping function of the interferogram to the ground-truth phase using the end-to-end convolutional neural network. With a well-trained deep convolutional neural network, this method can retrieve the phase from one-frame blindly phase-shifted interferogram, without phase unwrapping. The feasibility and applicability of the proposed method are verified by the simulation experiments of the microsphere and white blood cells, respectively. This method will pave the way to the quantitative phase imaging of biological cells with complex substructures.},
 type={artykuł},
 title={Phase retrieval without phase unwrapping for white blood cells in deep-learning phase-shifting digital holography},
 keywords={optyka, digital holography, phase retrieval, deep learning},
}