Deep Multi-Feature Transfer Network for Fourier Ptychographic Microscopy Imaging Reconstruction

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 3; p. 1237
• Main Authors: Wang, Xiaoli, Piao, Yan, Yu, Jinyang, Li, Jie, Sun, Haixin, Jin, Yuanshang, Liu, Limin, Xu, Tingfa
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 06-02-2022; MDPI
• Subjects: Algorithms; Aperture; Computer Simulation; Coordinate transformations; Datasets; Deep learning; deep multi-feature; Fourier ptychographic microscopy; Fourier transforms; High resolution; Image quality; Imaging techniques; Light; Light-emitting diodes; Microscope and microscopy; Microscopy; neural network; Neural networks; Neural Networks, Computer; Research Design; Sensors; Simulation methods; transfer learning; transfer learning; Fourier ptychographic microscopy; deep multi-feature; neural network
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22031237

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.