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Register a new userSingle-pixel ghost microscopy based on compressed sensing and complementary modulation
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An experiment demonstrating single-pixel single-arm complementary compressive microscopic ghost imaging based on a digital micromirror device (DMD) has been performed. To solve the difficulty of projecting speckles or modulated light patterns onto tiny biological objects, we instead focus the microscopic image onto the DMD. With this system, we have successfully obtained a magnified image of micron-sized objects illuminated by the microscopes own incandescent lamp. The image quality of our scheme is more than an order of magnitude better than that obtained by conventional compressed sensing with the same total sampling rate, and moreover, the system is robust against intensity instabilities of the light source and may be used under very weak light conditions. Since only one reflection direction of the DMD is used, the other reflection arm is left open for future infrared light sampling. This represents a big step forward toward the practical application of compressive microscopic ghost imaging in the biological and material science fields.
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Dynamic color modulation in the composite structure of graphene microelectromechanical systems (MEMS)- photonic crystal microcavity is investigated in this work. The designed photonic crystal microcavity has three resonant standing wave modes corresponding to the three primary colors of red (R), green (G) and blue (B), forming strong localization of light in three modes at different positions of the microcavity. Once graphene is added, it can govern the transmittance of three modes. When graphene is located in the abdomen of the standing wave, which has strong light absorption and therefore the structures transmittance is lower, or when graphene is located in the node of the standing wave, it has weak light absorption and therefore the structures transmittance is higher. Therefore, the graphene absorption of different colors of light can be regulated dynamically by applying voltages to tune the equilibrium position of the graphene MEMS in the microcavity, consequently realizing the output of vivid monochromatic light or multiple mixed colors of light within a single pixel, thus greatly improving the resolution. Our work provides a route to dynamic color modulation with graphene and provides guidance for the design and manufacture of ultrahigh resolution, ultrafast modulation and wide color gamut interferometric modulator displays.
Single-pixel cameras based on the concepts of compressed sensing (CS) leverage the inherent structure of images to retrieve them with far fewer measurements and operate efficiently over a significantly broader spectral range than conventional silicon-based cameras. Recently, photonic time-stretch (PTS) technique facilitates the emergence of high-speed single-pixel cameras. A significant breakthrough in imaging speed of single-pixel cameras enables observation of fast dynamic phenomena. However, according to CS theory, image reconstruction is an iterative process that consumes enormous amounts of computational time and cannot be performed in real time. To address this challenge, we propose a novel single-pixel imaging technique that can produce high-quality images through rapid acquisition of their effective spatial Fourier spectrum. We employ phase-shifting sinusoidal structured illumination instead of random illumination for spectrum acquisition and apply inverse Fourier transform to the obtained spectrum for image restoration. We evaluate the performance of our prototype system by recognizing quick response (QR) codes and flow cytometric screening of cells. A frame rate of 625 kHz and a compression ratio of 10% are experimentally demonstrated in accordance with the recognition rate of the QR code. An imaging flow cytometer enabling high-content screening with an unprecedented throughput of 100,000 cells/s is also demonstrated. For real-time imaging applications, the proposed single-pixel microscope can significantly reduce the time required for image reconstruction by two orders of magnitude, which can be widely applied in industrial quality control and label-free biomedical imaging.
In applications of scanning probe microscopy, images are acquired by raster scanning a point probe across a sample. Viewed from the perspective of compressed sensing (CS), this pointwise sampling scheme is inefficient, especially when the target image is structured. While replacing point measurements with delocalized, incoherent measurements has the potential to yield order-of-magnitude improvements in scan time, implementing the delocalized measurements of CS theory is challenging. In this paper we study a partially delocalized probe construction, in which the point probe is replaced with a continuous line, creating a sensor which essentially acquires line integrals of the target image. We show through simulations, rudimentary theoretical analysis, and experiments, that these line measurements can image sparse samples far more efficiently than traditional point measurements, provided the local features in the sample are enough separated. Despite this promise, practical reconstruction from line measurements poses additional difficulties: the measurements are partially coherent, and real measurements exhibit nonidealities. We show how to overcome these limitations using natural strategies (reweighting to cope with coherence, blind calibration for nonidealities), culminating in an end-to-end demonstration.
Compressed sensing fluorescence microscopy (CS-FM) proposes a scheme whereby less measurements are collected during sensing and reconstruction is performed to recover the image. Much work has gone into optimizing the sensing and reconstruction portions separately. We propose a method of jointly optimizing both sensing and reconstruction end-to-end under a total measurement constraint, enabling learning of the optimal sensing scheme concurrently with the parameters of a neural network-based reconstruction network. We train our model on a rich dataset of confocal, two-photon, and wide-field microscopy images comprising of a variety of biological samples. We show that our method outperforms several baseline sensing schemes and a regularized regression reconstruction algorithm.
An ultrafast single-pixel optical 2D imaging system using a single multimode fiber (MF) is proposed. The MF acted as the all-optical random pattern generator. Light with different wavelengths pass through a single MF will generator all-optical random speckle patterns, which have a low correlation of 0.074 with 0.1nm wavelength step from 1518.0nm to 1567.9nm. The all-optical random speckle patterns are perfect for compressive sensing (CS) imaging with the advantage of low cost in comparison with the conventional expensive pseudorandom binary sequence (PRBS). Besides, with the employment of photonic time stretch (PTS), light of different wavelengths will go through a single capsuled MF in time serial within a short pulse time, which makes ultrafast single-pixel all-optical CS imaging possible. In our work, the all-optical random speckle patterns are analyzed and used to perform CS imaging in our proposed system and the results shows a single-pixel photo-detector can be employed in CS imaging system and a 27 by 27 pixels image is reconstructed within 500 measurements. In our proposed imaging system, the fast Fourier transform (FFT) spatial resolution, which is a combination of multiple Gaussians, is analyzed. Considering 4 optical speckle patterns, the FFT spatial resolution is 50 by 50 pixels. This resolution limit has been obtained by removing the central low frequency components and observing the significant spectral power along all the radial directions.
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