Lensless systems based on ptychographic imaging can simultaneously achieve a large field of view and high resolution while having the advantages of small size, portability, and low cost compared to traditional lensed imaging. However, lensless imaging systems are susceptible to environmental noise and have a lower resolution of individual images than lens-based imaging systems, which means that they require a longer time to obtain a good result. Therefore, in this paper, to improve the convergence rate and robustness of noise in lensless ptychographic imaging, we propose an adaptive correction method, in which we add an adaptive error term and noise correction term in lensless ptychographic algorithms to reach convergence faster and create a better suppression effect on both Gaussian noise and Poisson noise. The Wirtinger flow and the Nesterov algorithms are used in our method to reduce computational complexity and improve the convergence rate. We applied the method to phase reconstruction for lensless imaging and demonstrated the effectiveness of the method by simulation and experiment. The method can be easily applied to other ptychographic iterative algorithms.

Hyperspectral resolution, high spatial resolution, and a wide field of view (FOV) are the targets of optical spectral microscopy imaging. However, hyperspectral microscopy imaging technology cannot provide a wide FOV and a high spatial resolution at the same time. Fourier ptychographic microscopy (FPM) is a novel microscopy imaging technique that uses LEDs at varying angles to capture a series of low-spatial-resolution images that are used to recover images that have both high spatial resolution and a wide FOV. Since FPM cannot obtain the spectral resolution of the sample, in this paper, an efficient strategy based on the FPM system is proposed for the reconstruction of hyperspectral images. First, the traditional FPM setup is optimized, with a new experimental setup based on halogen lamp illumination and a narrow band-pass filter to capture a series of low-spatial-resolution images at different wavelengths. Second, a new algorithm, combining hyperspectral resolution imaging using interpolation compensation and a phase retrieval algorithm, is proposed to reconstruct high-spatial-resolution, wide FOV, and hyperspectral resolution images. Finally, we verified the feasibility and effectiveness of our experimental setup and algorithm by both simulation and experiment. The results show that our method can not only reconstruct high-spatial-resolution and wide FOV images, but also has a spectral resolution of 5 nm.

A Fourier ptychographic microscope (FPM) can obtain images with high resolution and a wide field of view (FOV). However, the time-consuming process of image acquisition and computation leads to low reconstruction efficiency. Therefore, we propose a state-multiplexed method through an optimized illumination pattern to accelerate FPM. First, to reduce the redundancy of the spectrum and analyze the impact of missing a certain sub-spectrum on overall spectrum reconstruction, we use an image quality evaluation method to obtain the differential expression between missing a certain LED lighting and all LED lighting. Second, we use the difference expression to select the important LEDs and obtain an optimized illumination pattern that lights up only the LEDs in the central area and the edge LEDs. Then, we update the multiplexing method with the new, to the best of our knowledge, illumination pattern and obtain satisfactorily reconstructed images. Finally, we validate the effectiveness and efficiency of our method with both simulation and experiments. Compared with the traditional method, our method accelerates the reconstruction speed of FPM while ensuring a large FOV and high resolution, saving about 73% of time.