{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T01:24:08Z","timestamp":1773019448718,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,6]],"date-time":"2022-02-06T00:00:00Z","timestamp":1644105600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jilin Provincial Department of Education","award":["2021JB502L06"],"award-info":[{"award-number":["2021JB502L06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fourier ptychographic microscopy (FPM) is a potential imaging technique, which is used to achieve wide field-of-view (FOV), high-resolution and quantitative phase information. The LED array is used to irradiate the samples from different angles to obtain the corresponding low-resolution intensity images. However, the performance of reconstruction still suffers from noise and image data redundancy, which needs to be considered. In this paper, we present a novel Fourier ptychographic microscopy imaging reconstruction method based on a deep multi-feature transfer network, which can achieve good anti-noise performance and realize high-resolution reconstruction with reduced image data. First, in this paper, the image features are deeply extracted through transfer learning ResNet50, Xception and DenseNet121 networks, and utilize the complementarity of deep multiple features and adopt cascaded feature fusion strategy for channel merging to improve the quality of image reconstruction; then the pre-upsampling is used to reconstruct the network to improve the texture details of the high-resolution reconstructed image. We validate the performance of the reported method via both simulation and experiment. The model has good robustness to noise and blurred images. Better reconstruction results are obtained under the conditions of short time and low resolution. We hope that the end-to-end mapping method of neural network can provide a neural-network perspective to solve the FPM reconstruction.<\/jats:p>","DOI":"10.3390\/s22031237","type":"journal-article","created":{"date-parts":[[2022,2,6]],"date-time":"2022-02-06T20:40:18Z","timestamp":1644180018000},"page":"1237","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Deep Multi-Feature Transfer Network for Fourier Ptychographic Microscopy Imaging Reconstruction"],"prefix":"10.3390","volume":"22","author":[{"given":"Xiaoli","family":"Wang","sequence":"first","affiliation":[{"name":"Information and Communication Engineering, Electronics Information Engineering College, Changchun University of Science and Technology, Changchun 130022, China"},{"name":"Electrical and Electronic Teaching Center, Electronics Information Engineering College, Changchun University, Changchun 130022, China"}]},{"given":"Yan","family":"Piao","sequence":"additional","affiliation":[{"name":"Information and Communication Engineering, Electronics Information Engineering College, Changchun University of Science and Technology, Changchun 130022, China"}]},{"given":"Jinyang","family":"Yu","sequence":"additional","affiliation":[{"name":"Electrical and Electronic Teaching Center, Electronics Information Engineering College, Changchun University, Changchun 130022, China"}]},{"given":"Jie","family":"Li","sequence":"additional","affiliation":[{"name":"Electrical and Electronic Teaching Center, Electronics Information Engineering College, Changchun University, Changchun 130022, China"}]},{"given":"Haixin","family":"Sun","sequence":"additional","affiliation":[{"name":"Electrical and Electronic Teaching Center, Electronics Information Engineering College, Changchun University, Changchun 130022, China"}]},{"given":"Yuanshang","family":"Jin","sequence":"additional","affiliation":[{"name":"Electrical and Electronic Teaching Center, Electronics Information Engineering College, Changchun University, Changchun 130022, China"}]},{"given":"Limin","family":"Liu","sequence":"additional","affiliation":[{"name":"Electrical and Electronic Teaching Center, Electronics Information Engineering College, Changchun University, Changchun 130022, China"}]},{"given":"Tingfa","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, D., Fu, T., Bi, G., Jin, L., and Zhang, X. (2020). Long-Distance Sub-Diffraction High-Resolution Imaging Using Sparse Sampling. Sensors, 20.","DOI":"10.3390\/s20113116"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1011005","DOI":"10.3788\/AOS201636.1011005","article-title":"Fourier Ptychographic Microscopy: Theory, Advances, and Applications","volume":"36","author":"Jiasong","year":"2016","journal-title":"Acta Opt. Sin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1038\/nphoton.2013.187","article-title":"Wide-field, high-resolution Fourier ptychographic microscopy","volume":"7","author":"Zheng","year":"2013","journal-title":"Nat. Photonics"},{"key":"ref_4","first-page":"1","article-title":"Breakthroughs in Photonics 2013: Fourier Ptychographic Imaging","volume":"6","author":"Guoan","year":"2014","journal-title":"IEEE Photonics J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1364\/OPN.25.4.000026","article-title":"Fourier Ptychographic Microscopy: A Gigapixel Superscope for Biomedicine","volume":"25","author":"Zheng","year":"2014","journal-title":"Opt. Photonics News"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"14099","DOI":"10.1364\/OE.27.014099","article-title":"Apodized coherent transfer function constraint for partially coherent Fourier ptychographic microscopy","volume":"27","author":"Xiong","year":"2019","journal-title":"Opt. Express"},{"key":"ref_7","first-page":"237","article-title":"A practical algorithm for the determination of phase from image and diffraction plane pictures","volume":"35","author":"Gerchberg","year":"1972","journal-title":"Optik"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2758","DOI":"10.1364\/AO.21.002758","article-title":"Phase retrieval algorithms: A comparison","volume":"21","author":"Fienup","year":"1982","journal-title":"Appl. Opt."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4960","DOI":"10.1364\/OE.22.004960","article-title":"Embedded pupil function recovery for Fourier ptychographic microscopy","volume":"22","author":"Ou","year":"2014","journal-title":"Opt. Express"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"32400","DOI":"10.1364\/OE.21.032400","article-title":"Adaptive system correction for robust Fourier ptychographic imaging","volume":"21","author":"Bian","year":"2013","journal-title":"Opt. Express"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1364\/BOE.7.001336","article-title":"Efficient positional misalignment correction method for Fourier ptychographic microscopy","volume":"7","author":"Sun","year":"2016","journal-title":"Biomed. Opt. Express"},{"key":"ref_12","first-page":"1109","article-title":"Precise brightfield localization alignment for Fourier ptychographic microscopy","volume":"10","author":"Zhang","year":"2017","journal-title":"IEEE Photonics J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"081106","DOI":"10.3788\/LOP57.081106","article-title":"An Efficient Fourier Ptychographic Microscopy Imaging Method Based on Angle Illumination Optimization","volume":"57","author":"Tong","year":"2020","journal-title":"Laser Optoelectron. Prog."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"071102","DOI":"10.3788\/LOP55.071102","article-title":"Fourier Ptychographic Microscopy Based on Rotating Arc-shaped Array of LEDs","volume":"55","author":"Ziqiang","year":"2018","journal-title":"Laser Optoelectron. Prog."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1364\/BOE.5.001757","article-title":"Spectrum multiplexing and coherent-state decomposition in Fourier ptychographic imaging","volume":"5","author":"Dong","year":"2014","journal-title":"Biomed. Opt. Express"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"31729","DOI":"10.1109\/ACCESS.2018.2841854","article-title":"Efficient Colorful Fourier Ptychographic Microscopy Reconstruction with Wavelet Fusion","volume":"6","author":"Zhang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4856","DOI":"10.1364\/OE.23.004856","article-title":"Fourier ptychographic reconstruction using Wirtinger flow optimization","volume":"23","author":"Bian","year":"2015","journal-title":"Opt. Express"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3306","DOI":"10.1364\/BOE.9.003306","article-title":"Solving Fourier ptychographic imaging problems via neural network modeling and TensorFlow","volume":"9","author":"Shaowei","year":"2018","journal-title":"Biomed. Opt. Express"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"036502","DOI":"10.1117\/1.JBO.26.3.036502","article-title":"Neural network model assisted Fourier ptychography with Zernike aberration recovery and total variation constraint","volume":"26","author":"Zhang","year":"2021","journal-title":"J. Biomed. Opt."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep Learning in Neural Networks: An Overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_22","unstructured":"Ying, T., Jian, Y., and Liu, X. (2017, January 21\u201326). Image Super-Resolution via Deep Recursive Residual Network. Proceedings of the IEEE Conference on Computer Vision & Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z. (2016). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, IEEE Computer Society.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Lee, K.M. (2017, January 21\u201326). Enhanced Deep Residual Networks for Single Image Super-Resolution. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, J., Chen, Z., Zhao, X., and Shao, L. (2020). MapGAN: An Intelligent Generation Model for Network Tile Maps. Sensors, 20.","DOI":"10.3390\/s20113119"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"17141","DOI":"10.1038\/lsa.2017.141","article-title":"Phase recovery and holographic image reconstruction using deep learning in neural networks","volume":"7","author":"Rivenson","year":"2017","journal-title":"Light Sci. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1364\/OPTICA.4.001117","article-title":"Lensless computational imaging through deep learning","volume":"4","author":"Sinha","year":"2017","journal-title":"Optica"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kappeler, A., Ghosh, S., Holloway, J., Cossairt, O., and Katsaggelos, A. (2017, January 17\u201320). Ptychnet: CNN based fourier ptychography, 2017. Proceedings of the IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296574"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"26470","DOI":"10.1364\/OE.26.026470","article-title":"Deep learning approach for Fourier ptychography microscopy","volume":"26","author":"Nguyen","year":"2018","journal-title":"Optics Express"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1364\/OE.27.000644","article-title":"Illumination Pattern Design with Deep Learning for Single-Shot Fourier Ptychographic Microscopy","volume":"27","author":"Cheng","year":"2019","journal-title":"Opt. Express"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yu, J., Li, J., Wang, X., Zhang, J., Liu, L., and Jin, Y. (2021, January 23\u201326). Microscopy image reconstruction method based on convolution network feature fusion. Proceedings of the 2021 International Conference on Electronic Information Engineering and Computer Science (EIECS), Changchun, China.","DOI":"10.1109\/EIECS53707.2021.9588124"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). In Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Laurens, V., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8612","DOI":"10.1364\/OE.27.008612","article-title":"Fourier ptychographic microscopy reconstruction with multiscale deep residual network","volume":"27","author":"Zhang","year":"2019","journal-title":"Opt. Express"},{"key":"ref_36","first-page":"920","article-title":"Research on face recognition based on convolutional neural network","volume":"10","author":"Zhao","year":"2018","journal-title":"Inf. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3365","DOI":"10.1109\/TPAMI.2020.2982166","article-title":"Deep Learning for Image Super-resolution: A Survey","volume":"43","author":"Wang","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3106","DOI":"10.1109\/TMM.2019.2919431","article-title":"Deep Learning for Single Image Super-Resolution: A Brief Review","volume":"21","author":"Yang","year":"2019","journal-title":"IEEE Trans. Multimedia"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"20724","DOI":"10.1364\/OE.24.020724","article-title":"Adaptive step-size strategy for noise-robust Fourier ptychographic microscopy","volume":"24","author":"Chao","year":"2016","journal-title":"Opt. Express"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image Quality Assessment: From Error Visibility to Structural Similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Processing"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/1237\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:15:05Z","timestamp":1760134505000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/1237"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,6]]},"references-count":40,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22031237"],"URL":"https:\/\/doi.org\/10.3390\/s22031237","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,6]]}}}