Physics‐based learning with channel attention for Fourier ptychographic microscopy

Abstract

Fourier ptychographic microscopy (FPM) is a computational imaging technology for large field‐of‐view, high resolution and quantitative phase imaging. In FPM, low‐resolution intensity images captured with angle‐varying illumination are synthesized in Fourier space with phase retrieval approaches. However, system errors such as pupil aberration and light‐emitting diode (LED) intensity error seriously affect the reconstruction performance. In this article, we propose a physics‐based neural network with channel attention for FPM reconstruction. With the channel attention module, which is introduced into physics‐based neural networks for the first time, the spatial distribution of LED intensity can be adaptively corrected. Besides, the channel attention module is used to synthesize different Zernike modes and recover the pupil function. Detailed simulations and experiments are carried out to validate the effectiveness and robustness of the proposed method. The results demonstrate that our method achieves better performance in high‐resolution complex field reconstruction, LED intensity correction and pupil function recovery compared with the state‐of‐art methods. The combination with deep neural network structures like channel attention modules significantly enhance the performance of physics‐based neural networks and will promote the application of FPM in practical use.

A Fourier Ptychographic Microscopy Reconstruction Method Based on SwinIR Physical Model

“…The dataset requires images with (a, b) pairs. Image 𝑎 can be estimated from the measured value 𝑏 by unsupervised backward propagation, which is called physics-based learning [7]. Since each image reconstruction result can be estimated independently, no training set is required.…”

3D microscopy in industrial measurements

“…Therefore, diverse forms of computational microscopy techniques have been devised, including digital holographic microscopy, 132 transport of intensity equation, 133 differential phase contrast microscopy, 134 lens-free on-chip holography 135 and Fourier ptychographic microscopy. 136 These methods invert the multimodal mathematical characterisation of the light field from the amplitude, phase, polarisation and other data acquired from the microscope, thereby providing remarkable advantages compared with traditional optical microscopy. Hence, modern microscopy is directed to understanding acquired data from a multidimensional perspective.…”

“…Computational microscopy, as a subfield of computational imaging, 131 has been widely studied to improve the resolution and contrast of traditional light microscopes by improving their multiangle information acquisition and fusion capabilities. Therefore, diverse forms of computational microscopy techniques have been devised, including digital holographic microscopy, 132 transport of intensity equation, 133 differential phase contrast microscopy, 134 lens‐free on‐chip holography 135 and Fourier ptychographic microscopy 136 . These methods invert the multimodal mathematical characterisation of the light field from the amplitude, phase, polarisation and other data acquired from the microscope, thereby providing remarkable advantages compared with traditional optical microscopy.…”

Fourier Ptychographic Neural Network Combined with Zernike Aberration Recovery and Wirtinger Flow Optimization

“…Hu et al [13] proposed a microscopic image aberration correction method based on deep learning and aberration prior knowledge, which enhances and corrects the microscopic image in the form of image restoration. Zhang et al [14] combined the channel attention module with a physics-based neural network to adaptively correct aberrations; Zhao et al [15] established the relationship between the phase and aberration coefficient through deep learning to segment samples and backgrounds [16] and realized fast automatic aberration compensation correction [17]. Wu et al [18] proposed an FPM aberration correction reconstruction framework (AA-P) algorithm based on an improved phase retrieval strategy, which improves the iterative reconstruction quality by optimizing the spectral function and the pupil function update strategy while alleviating the influence of mixed wavefront aberrations on the reconstructed image quality and avoiding the occurrence of errors in the reconstruction process.…”