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Fourier ptychography microscopy (FPM) is a new computational imaging technique that can provide gigapixel images with both high resolution and a wide field of view (FOV). However, time consuming of the data-acquisition process is a critical issue. In this paper, we make an analysis on the FPM imaging system with half number of the captured images. Based on the image analysis of the conventional FPM system, we then compare the reconstructed images with different number of captured data. Simulation and experiment results show that the reconstructed image with half number captured data do not show obvious resolution degradation compared to that with all the captured data, except a contrast reduction. In particular in the case when the object is close to phase-only/amplitude only, the quality of the reconstructed image with half of the captured data is nearly as good as the one reconstructed with full data.

Fourier ptychographi cmicroscopy(FPM) is a newly developed computational imaging technique that can provide gigapixel images with both high resolution (HR) and wide field of view (FOV). However, the positional misalignment of the LED array induces a degradation of the reconstruction, especially in the regions away from the optical axis. In this paper, we propose a robust and fast method to correct the LED misalignment of FPM, termed as misalignment correction for FPM (mcFPM). Although different regions in the FOV have different sensitivity to the LED misalignment, the experimental results show that mcFPM is robust to eliminate the degradation in each region. Compared with the state-of-the-art methods, mcFPM is much faster.

Imaging through scattering media is encountered in many disciplines or sciences, ranging from biology, mesescopic physics and astronomy. But it is still a big challenge because light suffers from multiple scattering is such media and can be totally decorrelated. Here, we propose a deep-learning-based method that can retrieve the image of a target behind a thick scattering medium. The method uses a trained deep neural network to fit the way of mapping of objects at one side of a thick scattering medium to the corresponding speckle patterns observed at the other side. For demonstration, we retrieve the images of a set of objects hidden behind a 3mm thick white polystyrene slab, the optical depth of which is 13.4 times of the scattering mean free path. Our work opens up a new way to tackle the longstanding challenge by using the technique of deep learning.

Light field reconstruction from images captured by focal plane sweeping, such as light field moment imaging (LFMI) and light field reconstruction with back projection (LFBP), can achieve high lateral resolution comparable to the modern camera sensor. This is impossible for the conventional lens array based light field capture systems. However, capturing a series of focal sweeping images along the optical axis is time consuming and requires fine alignment, and different focal plane based light field reconstruction techniques require images with different characteristics. To solve this problem, we present an efficient approach for fast light field acquisition with precise focal plane sweeping capture by defocus modulation rather than axial movement. Because of the controllable point spread function, we can capture images for light field reconstruction with both LFMI and LFBP.

Light field reconstruction from images captured by focal plane sweeping can achieve high lateral resolution comparable to the modern camera sensor. This is impossible for the conventional micro-lenslet based light field capture systems. However, the severe defocus noise and the low depth resolution limit its applications. In this paper, we analyze the defocus noise and the depth resolution in the focal plane sweeping based light field reconstruction technique, and propose a method to reduce the defocus noise and improve the depth resolution. Both numerical and experimental results verify the proposed method.

Current multispectral imagers suffer from low photon efficiency and limited spectrum range. These limitations are partially due to the technological limitations from array sensors (CCD or CMOS), and also caused by separative measurement of the entries/slices of a spatial-spectral data cube. Besides, they are mostly expensive and bulky. To address above issues, this paper proposes to image the 3D multispectral data with a single bucket detector in a multiplexing way. Under the single pixel imaging scheme, we project spatial-spectral modulated illumination onto the target scene to encode the scene's 3D information into a 1D measurement sequence. Conventional spatial modulation is used to resolve the scene's spatial information. To avoid increasing requisite acquisition time for 2D to 3D extension of the latent data, we conduct spectral modulation in a frequency-division multiplexing manner in the speed gap between slow spatial light modulation and fast detector response. Then the sequential reconstruction falls into a simple Fourier decomposition and standard compressive sensing problem. A proof-of-concept setup is built to capture the multispectral data (64 pixels $\times$ 64 pixels $\times$ 10 wavelength bands) in the visible wavelength range (450nm-650nm) with acquisition time being 1 minute. The imaging scheme is of high flexibility for different spectrum ranges and resolutions. It holds great potentials for various low light and airborne applications, and can be easily manufactured production-volume portable multispectral imagers.

Fourier Ptychography (FP) is a recently proposed technique for large field of view and high resolution imaging. Specifically, FP captures a set of low resolution images under angularly varying illuminations and stitches them together in Fourier domain. One of FP's main disadvantages is its long capturing process due to the requisite large number of incident illumination angles. In this letter, utilizing the sparsity of natural images in Fourier domain, we propose a highly efficient method termed as AFP, which applies content adaptive sparse illumination for Fourier ptychography by capturing the most informative parts of the scene's spatial spectrum. We validate the effectiveness and efficiency of the reported framework with both simulations and real experiments. Results show that the proposed AFP could shorten the acquisition time of conventional FP by around 30%-60%.

Phase transitions give crucial insight into many-body systems, as crossovers between different regimes of order are determined by the underlying dynamics. These dynamics, in turn, are often constrained by dimensionality and geometry. For example, in one- and two-dimensional systems with continuous symmetry, thermal fluctuations prevent the formation of long-range order[1,2]. Two-dimensional systems are particularly significant, as vortices can form in the plane but cannot tilt out of it. At high temperatures, random motion of these vortices destroys large-scale coherence. At low temperatures, vortices with opposite spin can pair together, cancelling their circulation and allowing quasi-long-range order to appear. This Berezenskii-Kosterlitz-Thouless (BKT) transition[3,4] is essentially classical, arising for example in the traditional XY model for spins, but to date experimental evidence has been obtained only in cold quantum systems. Measurements of superfluid sound speed[5] and critical velocity[6] have been consistent with scaling predictions, and vortices have been observed directly in cold atom experiments[7,8]. However, the presence of trapping potentials restricts measurement to vortex density, rather than number, and obscures the process of vortex unbinding. Further, atom and fluid experiments suffer from parasitic heating and difficulties in phase recording, leading to results that differ from theory in many quantitative aspects. Here, we use a nonlinear optical system to directly observe the ideal BKT transition, including vortex pair dynamics and the correlation properties of the wavefunction, for both repulsive and attractiveinteractions (the photonic equivalent of ferromagnetic and antiferromagnetic conditions[9]). The results confirm the thermodynamics of the BKT transition and expose outstanding issues in the crossovers to superfluidity and Bose-Einstein condensation., Comment: 7 pages, 4 figures

A cascaded iterative Fourier transform (CIFT) algorithm is presented for optical security applications. Two phase-masks are designed and located in the input and the Fourier domains of a 4-f correlator respectively, in order to implement the optical encryption or authenticity verification. Compared with previous methods, the proposed algorithm employs an improved searching strategy: modifying the phase-distributions of both masks synchronously as well as enlarging the searching space. Computer simulations show that the algorithm results in much faster convergence and better image quality for the recovered image. Each of these masks is assigned to different person. Therefore, the decrypted image can be obtained only when all these masks are under authorization. This key-assignment strategy may reduce the risk of being intruded., Comment: 18 pages, 4 figures, 2 tables. submitted to Optik