Abstract:Recently Fourier Ptychography (FP) has attracted great attention, due to its marked effectiveness in leveraging snapshot numbers for spatial resolution in large field-of-view imaging. To acquire high signalto-noise-ratio (SNR) images under angularly varying illuminations for subsequent reconstruction, FP requires long exposure time, which largely limits its practical applications. In this paper, based on the recently reported Wirtinger flow algorithm, we propose an iterative optimization framework incorporating phase retrieval and noise relaxation together, to realize FP reconstruction using low SNR images captured under short exposure time. Experiments on both synthetic and real captured data validate the effectiveness of the proposed reconstruction method. Specifically, the proposed technique could save ∼ 80% exposure time to achieve similar retrieval accuracy compared to the conventional FP. Besides, we have released our source code for non-commercial use.

Existing multispectral imagers mostly use available array sensors to separately measure 2D data slices in a 3D spatial-spectral data cube. Thus they suffer from low photon efficiency, limited spectrum range and high cost. To address these issues, we propose to conduct multispectral imaging using a single bucket detector, to take full advantage of its high sensitivity, wide spectrum range, low cost, small size and light weight. Technically, utilizing the detector’s fast response, a scene’s 3D spatial-spectral information is multiplexed into a dense 1D measurement sequence and then demultiplexed computationally under the single pixel imaging scheme. A proof-of-concept setup is built to capture multispectral data of 64 pixels × 64 pixels × 10 wavelength bands ranging from 450 nm to 650 nm, with the acquisition time being 1 minute. The imaging scheme holds great potentials for various low light and airborne applications, and can be easily manufactured as production-volume portable multispectral imagers.

The quantum dot spectrometer, fabricated by integrating different quantum dots with an image sensor to reconstruct the target spectrum from spectral-coupled measurements, is an emerging and promising hyperspectrometry technology with high resolution and a compact size. The spectral resolution and spectral range of quantum dot spectrometers have been limited by the spectral variety of the available quantum dots and the robustness of algorithmic reconstruction. Moreover, the spectrometer integration of quantum dots also suffers from inherent photoluminescence emission and poor batch-to-batch repeatability. In this work, we developed nonemissive in situ fabricated MA3Bi2X9 and Cs2SnX6 (MA = CH3NH3; X = Cl, Br, I) perovskite-quantum-dot-embedded films (PQDFs) with precisely tunable transmittance spectra for quantum dot spectrometer applications. The resulting PQDFs contain in situ fabricated perovskite nanocrystals with homogenous dispersion in a polymeric matrix, giving them advantageous features such as high transmittance efficiency and good batch-to-batch repeatability. By integrating a filter array of 361 kinds of PQDFs with a silicon-based photodetector array, we successfully demonstrated the construction of a perovskite quantum dot spectrometer combined with a compressive-sensing-based total-variation optimization algorithm. A spectral resolution of ~1.6 nm was achieved in the broadband of 250–1000 nm. The performance of the perovskite quantum dot spectrometer is well beyond that of human eyes in terms of both the spectral range and spectral resolution. This advancement will not only pave the way for using quantum dot spectrometers for practical applications but also significantly impact the development of artificial intelligence products, clinical treatment equipment, scientific instruments, etc.

Single-pixel imaging (SPI) is a novel technique that captures 2D images using a photodiode, instead of conventional 2D array sensors. SPI has high signal-to-noise ratio, wide spectral range, low cost, and robustness to light scattering. Various algorithms have been proposed for SPI reconstruction, including linear correlation methods, the alternating projection (AP) method, and compressive sensing (CS) based methods. However, there has been no comprehensive review discussing respective advantages, which is important for SPI's further applications and development. In this paper, we review and compare these algorithms in a unified reconstruction framework. We also propose two other SPI algorithms, including a conjugate gradient descent (CGD) based method and a Poisson maximum-likelihood-based method. Both simulations and experiments validate the following conclusions: to obtain comparable reconstruction accuracy, the CS-based total variation (TV) regularization method requires the fewest measurements and consumes the least running time for small-scale reconstruction, the CGD and AP methods run fastest in large-scale cases, and the TV and AP methods are the most robust to measurement noise. In a word, there are trade-offs in capture efficiency, computational complexity, and robustness to noise among different SPI algorithms. We have released our source code for non-commercial use.

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%.

Fourier ptychographic microscopy (FPM) is a novel computational coherent imaging technique for high space-bandwidth product imaging. Mathematically, Fourier ptychographic (FP) reconstruction can be implemented as a phase retrieval optimization process, in which we only obtain low resolution intensity images corresponding to the sub-bands of the sample’s high resolution (HR) spatial spectrum, and aim to retrieve the complex HR spectrum. In real setups, the measurements always suffer from various degenerations such as Gaussian noise, Poisson noise, speckle noise and pupil location error, which would largely degrade the reconstruction. To efficiently address these degenerations, we propose a novel FP reconstruction method under a gradient descent optimization framework in this paper. The technique utilizes Poisson maximum likelihood for better signal modeling, and truncated Wirtinger gradient for effective error removal. Results on both simulated data and real data captured using our laser-illuminated FPM setup show that the proposed method outperforms other state-of-the-art algorithms. Also, we have released our source code for non-commercial use.

High-throughput computational imaging requires efficient processing algorithms to retrieve multi-dimensional and multi-scale information. In computational phase imaging, phase retrieval (PR) is required to reconstruct both amplitude and phase in complex space from intensity-only measurements. The existing PR algorithms suffer from the tradeoff among low computational complexity, robustness to measurement noise and strong generalization on different modalities. In this work, we report an efficient large-scale phase retrieval technique termed as LPR. It extends the plug-and-play generalized-alternating-projection framework from real space to nonlinear complex space. The alternating projection solver and enhancing neural network are respectively derived to tackle the measurement formation and statistical prior regularization. This framework compensates the shortcomings of each operator, so as to realize high-fidelity phase retrieval with low computational complexity and strong generalization. We applied the technique for a series of computational phase imaging modalities including coherent diffraction imaging, coded diffraction pattern imaging, and Fourier ptychographic microscopy. Extensive simulations and experiments validate that the technique outperforms the existing PR algorithms with as much as 17dB enhancement on signal-to-noise ratio, and more than one order-of-magnitude increased running efficiency. Besides, we for the first time demonstrate ultra-large-scale phase retrieval at the 8K level ($$7680\times 4320$$ 7680 × 4320 pixels) in minute-level time.

Single pixel imaging (SPI) is a novel technique being able to capture 2D images using a bucket detector with high signal-to-noise ratio, wide spectrum range and low cost. Conventional SPI projects random illumination patterns to randomly and uniformly sample the entire scene's information. Determined by the Nyquist sampling theory, SPI needs either numerous projections or high computation cost to reconstruct the target scene, especially for high-resolution cases. To address this issue, we propose an efficient single pixel imaging technique (eSPI), which instead projects sinusoidal patterns for importance sampling of the target scene's spatial spectrum in Fourier space. Specifically, utilizing the centrosymmetric conjugation and sparsity priors of natural images' spatial spectra, eSPI sequentially projects two π 2 -phase-shifted sinusoidal patterns to obtain each Fourier coefficient in the most informative spatial frequency bands. eSPI can reduce requisite patterns by two orders of magnitude compared to conventional SPI, which helps a lot for fast and high-resolution SPI.