Single-shot phase retrieval via Fourier ptychographic microscopy

Fourier ptychographic microscopy (FPM) is a newly developed microscopic technique for large field of view, high-resolution and quantitative phase imaging by combining the techniques from ptychographic imaging, aperture synthesizing and phase retrieval. In FPM, an LED array is utilized to illuminate the specimen from different angles and the corresponding intensity images are synthesized to reconstruct a high-resolution complex field. As a flexible and low-cost approach to achieve high-resolution, wide-field and quantitative phase imaging, FPM is of enormous potential in biomedical applications such as hematology and pathology. Conventionally, the FPM reconstruction problem is solved by using a phase retrieval method, termed Alternate Projection. By iteratively updating the Fourier spectrum with low-resolution-intensity images, the result converges to a high-resolution complex field. Here we propose a new FPM reconstruction framework with deep learning methods and design a multiscale, deep residual neural network for FPM reconstruction. We employ the widely used open-source deep learning library PyTorch to train and test our model and carefully choose the hyperparameters of our model. To train and analyze our model, we build a large-scale simulation dataset with an FPM imaging model and an actual dataset captured with an FPM system. The simulation dataset and actual dataset are separated as training datasets and test datasets, respectively. Our model is trained with the simulation training dataset and fine tuned with the fine-tune dataset, which contains actual training data. Both our model and the conventional method are tested on the simulation test dataset and the actual test dataset to evaluate the performances. We also show the reconstruction result of another neural network-based method for comparison. The experiments demonstrate that our model achieves better reconstruction results and consumes much less time than conventional methods. The results also point out that our model is more robust under system aberrations such as noise and blurring (fewer intensity images) compared with conventional methods.

Fourier ptychographic microscopy (FPM) is an attractive method to extend the resolution beyond the conventional limit defined by a microscope optics, sharing properties with ptychographic, synthetic aperture imaging and phase retrieval. The algorithm uses a sequence of low-resolution (LR) images acquired under angularly varying illumination to reconstruct a high-resolution (HR) image. However, traditional FPM may trap to the sub-optimal solution, since brute-force replacement in the Fourier domain is applied. To address this problem, we propose here a weighted replacement for Fourier ptychographic microscopy (WFPM). We employ the weighted average of spectrums corresponding to different illumination angle to replace the overlapped regions in the Fourier domain. A series of experimental results demonstrate that the reconstructed image using the proposed WFPM shows a better quality and a faster convergence compared with the results obtained by FPM.

Adaptive denoising method for Fourier ptychographic microscopy

White blood cells (WBC) play a significant role in human immune system, so WBC detection is a meaningful work. In this paper, we propose a novel framework which combines Fourier ptychographic microscopy (FPM) and SO-you only look once (YOLO) for WCB detection. FPM is a recently developed microscope technology which can achieve high-resolution, wide-field, and quantitative phase imaging at the same time without mechanical moving and phase measurement. With the FPM, we can get high resolution, wide field-of-view blood cell images at one time. After obtaining high-resolution images, we propose SO-YOLO as our detection method. As a convolutional neural network, the SO-YOLO outperforms the state-of-the-art detection methods both in accuracy and speed. The proposed framework in this paper can be used in clinical diagnose, because the experiment setup is flexible and the detection is fast. The data set in this paper is made with FPM by ourselves. Our data set contains 1000 color high resolution images and all the WBC has been detected and counted by a human expert. The detection results by the experts are regarded as the ground truth. The experiment results show that the SO-YOLO performs well in detecting small objects compared with other methods.

Fourier ptychography microscopy (FPM) has made significant progress since its invention in 2013, thanks to its adaptable nature, high resolution, and vast field-of-view capabilities. FPM is used in various medical applications across multiple optical wavelengths, from automated digital pathology to radiology and ultraviolet label-free imaging. This review explores the fundamental physical and computational concepts that have driven advancements in digital pathology using FPM. A crucial part of the progress has been the development of computational algorithms, which have directly contributed to the improvements in FPM. We evaluate early-stage algorithms like the Gerchberg-Saxton and highlight how phase-retrieval and deep-learning advancements have propelled FPM forward. Additionally, we discuss the impact of these algorithms on digital pathology for potential automated diagnosis, providing a comprehensive explanation of their influence on medical imaging and offering insights into future researchdirections.

The integration of neural network and physical reconstruction model for Fourier ptychographic microscopy

Fourier ptychographic microscopy (FPM) emerged as a prominent imaging technique in 2013, attracting significant interest due to its remarkable features such as precise phase retrieval, expansive field of view (FOV), and superior resolution. Over the past decade, FPM has become an essential tool in microscopy, with applications in metrology, scientific research, biomedicine, and inspection. This achievement arises from its ability to effectively address the persistent challenge of achieving a trade-off between FOV and resolution in imaging systems. It has a wide range of applications, including label-free imaging, drug screening, and digital pathology. In this comprehensive review, we present a concise overview of the fundamental principles of FPM and compare it with similar imaging techniques. In addition, we present a study on achieving colorization of restored photographs and enhancing the speed of FPM. Subsequently, we showcase several FPM applications utilizing the previously described technologies, with a specific focus on digital pathology, drug screening, and three-dimensional imaging. We thoroughly examine the benefits and challenges associated with integrating deep learning and FPM. To summarize, we express our own viewpoints on the technological progress of FPM and explore prospective avenues for its future developments.

As a typical computational method, Fourier ptychographic microscopy (FPM) can realize high spatial resolution and quantitative phase imaging while preserving the large field of view with a low numerical aperture (NA) objective. A programmable light-emitting diode (LED) array is used as a typical illuminator in an FPM system, and the illumination parameters of each LED element are crucial to the success of the FPM reconstruction algorithm. Compared with LED arrays arranged in rectangular arrays, LED arrays with special structures such as domes or rings can effectively improve FPM imaging results and imaging efficiency. As a trade-off, their calibration difficulty is greatly increased due to the lack of geometric constraints of rectangular arrays. In this paper, we propose an effective hybrid full-pose parameter calibration method for freeform LED array illuminators, combining stereoscopic 3D imaging techniques and the geometric constraints of the microscopic platform. First, a stereovision system is used to obtain the accurate 3D position of each LED element of the freeform illuminator and to construct a rigid 3D coordinate LED array system. Then, calibration between the coordinate system of the LED array and that of the optical imaging component is realized according to the geometric features of the brightfield-to-darkfield edges. Finally, we verify the feasibility and effectiveness of the proposed method through full-pose parameter calibration of LED arrays with different arrangement rules.

Fourier Ptychographic Microscopy (FPM) and Differential Phase Contrast (DPC) are quantitative phase imaging (QPI) methods that recover the complex transmittance function of a sample through coded illumination measurements and phase retrieval optimization. The successes of these methods rely upon acquiring several or possibly hundreds of illumination-encoded measurements. The multi-shot nature of such methods limits their temporal resolution. Similar to motion-induced blur during a long photographic exposure, motion occurring during these acquisitions causes spatial distortion and errors in the reconstructed phase, which inhibits these methods' ability to image fast moving live samples. Here we present a novel approach to correct for motion during QPI capture that relies on motion navigation to register measurements together prior to phase retrieval. The different illumination patterns required for QPI cause the measurements to have a different contrasts. This makes it difficult to use standard registration approaches to estimate complex sample motion directly from the measurements. Instead, we use a color-multiplexed navigator signal (red) that is comprised of a constant illumination pattern and leverage a color camera to separate it from the primary QPI information (green). The reliable motion estimate allows measurements to be shared across time points through image registration. This enables a full set of measurements for a phase retrieval problem to be solved at each time point. We demonstrate proof-of-concept experimental results in which blurring due to live sample motion (swimming Zebra fish, cell motion, and organelle movement) is reduced.

I will report on a new computational method for microscopy, Fourier Ptychographic Microscopy. This method performs phase retrieval through illumination angle diversity and allows us to transform a standard microscope with a 0.08 NA objective into a high-performance gigapixel microscope with 0.8 micron resolution, field-of-view of 120 mm2 and a long depth-of-field of 0.3 mm. This method transform the general challenge of standard microscopy from one coupled to the physical limitations of the system's optics to one that is computationally solvable. The method can significantly simplify digital pathology procedures.

Fourier ptychographic microscopy (FPM) is a recently proposed computational imaging method that achieves both high resolution (HR) and wide field of view. In the conventional FPM model, the sample is assumed to be a 2D thin layer, and a series of low-resolution images at different illumination angles is used for HR image reconstruction. However, in practice, the sample complex amplitude distribution is usually mixed with some unknown background interferences. These background interferences may result from inhomogeneous media distribution or other defocusing layers, etc. The background interference will significantly degrade FPM reconstruction results, but so far it is not considered in the conventional FPM algorithm. Here, we propose a method that adaptively separates background interferences for each illumination angle. Experimental results show that the proposed method has a faster convergence speed and better reconstruction accuracy than the conventional FPM model.

Fourier Ptychographic Microscopy (FPM) is a computational imaging method that is able to super-resolve features beyond the diffraction-limit set by the objective lens of a traditional microscope. This is accomplished by using synthetic aperture and phase retrieval algorithms to combine many measurements captured by an LED array microscope with programmable source patterns. FPM provides simultaneous large field-of-view and high resolution imaging, but at the cost of reduced temporal resolution, thereby limiting live cell applications. In this work, we learn LED source pattern designs that compress the many required measurements into only a few, with negligible loss in reconstruction quality or resolution. This is accomplished by recasting the super-resolution reconstruction as a Physics-based Neural Network and learning the experimental design to optimize the network’s overall performance. Specifically, we learn LED patterns for different applications (e.g. amplitude contrast and quantitative phase imaging) and show that the designs we learn through simulation generalize well in the experimental setting. Further, we discuss a context-specific loss function, practical memory limitations, and interpretability of our learned designs.

Fourier ptychographic microscopy (FPM) imaging is a computational imaging technology that can reconstruct wide-field high-resolution (HR) images. It uses a series of low-resolution images captured by a camera under different illumination angles. The images are stitched in the Fourier domain to expand their spectral range. Under high-angle illumination, a dark-field image is noisy with a low signal-to-noise ratio, which significantly reduces the reconstruction quality of FPM. Conventional reconstruction algorithms often have low FPM imaging performance and efficiency due to optimization strategies. In response to these problems, this paper proposes an FPM imaging method based on an improved phase recovery strategy to optimize the alternating iterative algorithm. The technique uses an improved threshold method to reduce noise in the image preprocessing stage to maximize the retention of high-frequency sample information. Moreover, an adaptive control factor is added in the subsequent iterative update process to balance the sample spectrum function. This study verifies the effectiveness of the proposed method on both simulation and experimental images. The results show that the proposed method can effectively suppress image background noise and has a faster convergence speed and higher robustness. In addition, it can be used to reconstruct HR complex amplitude images of objects under wide field-of-view conditions.

Fourier ptychography microscopy(FPM) is a recently developed computational imaging approach for microscopic super-resolution imaging. By turning on each light-emitting-diode (LED) located on different position on the LED array sequentially and acquiring the corresponding images that contain different spatial frequency components, high spatial resolution and quantitative phase imaging can be achieved in the case of large field-of-view. Nevertheless, FPM has high requirements for the system construction and data acquisition processes, such as precise LEDs position, accurate focusing and appropriate exposure time, which brings many limitations to its practical applications. In this paper, inspired by artificial neural network, we propose a Fourier ptychography multi-parameter neural network (FPMN) with composite physical prior optimization. A hybrid parameter determination strategy combining physical imaging model and data-driven network training is proposed to recover the multi layers of the network corresponding to different physical parameters, including sample complex function, system pupil function, defocus distance, LED array position deviation and illumination intensity fluctuation, etc. Among these parameters, LED array position deviation is recovered based on the features of brightfield to darkfield transition low-resolution images while the others are recovered in the process of training of the neural network. The feasibility and effectiveness of FPMN are verified through simulations and actual experiments. Therefore FPMN can evidently reduce the requirement for practical applications of FPM.

Red blood cell (RBC) counting is of great medical significance in clinical examination. Commonly, the cell counting task is completed by microscopic examination, which requires a high resolution. This paper proposes an automatic counting system of red blood cells based on Fourier ptychographic microscopy (FPM) and estimates the RBC number via a convolutional neural network (CNN). The counting network is based on a regression model, using a VGG-16 network combined with a feature pyramid network (FPN). The experimental results show that the mean absolute percentage error (MAPE) of our counting network can reach 0.86%, which means a high accuracy.