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1. Wave optics of imaging with contact ball lenses

   https://doi.org/10.1038/s41598-023-32826-8  

   Maslov, A V; Jin, B; Astratov, V N (December 2023, Scientific Reports)

   Abstract Recent progress in microspherical superlens nanoscopy raises a fundamental question about the transition from super-resolution properties of mesoscale microspheres, which can provide a subwavelength resolution$$\sim \lambda /7$$ $\sim \lambda /7$, to macroscale ball lenses, for which the imaging quality degrades because of aberrations. To address this question, this work develops a theory describing the imaging by contact ball lenses with diameters$$30 $30<D/\lambda <4000$covering this transition range and for a broad range of refractive indices$$1.3<2.1$$ $1.3<n<2.1$. Starting from geometrical optics we subsequently proceed to an exact numerical solution of the Maxwell equations explaining virtual and real image formation as well as magnificationMand resolution near the critical index$$n\approx 2$$ $n\approx 2$which is of interest for applications demanding the highestMsuch as cellphone microscopy. The wave effects manifest themselves in a strong dependence of the image plane position and magnification on$$D/\lambda $$ $D/\lambda$, for which a simple analytical formula is derived. It is demonstrated that a subwavelength resolution is achievable at$$D/\lambda \lesssim 1400$$ $D/\lambda \lesssim 1400$. The theory explains the results of experimental contact-ball imaging. The understanding of the physical mechanisms of image formation revealed in this study creates a basis for developing applications of contact ball lenses in cellphone-based microscopy. 

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2. Deep-learning-based image reconstruction for compressed ultrafast photography

   https://doi.org/10.1364/OL.397717  

   Ma, Yayao; Feng, Xiaohua; Gao, Liang (August 2020, Optics Letters)

   Compressed ultrafast photography (CUP) is a computational optical imaging technique that can capture transient dynamics at an unprecedented speed. Currently, the image reconstruction of CUP relies on iterative algorithms, which are time-consuming and often yield nonoptimal image quality. To solve this problem, we develop a deep-learning-based method for CUP reconstruction that substantially improves the image quality and reconstruction speed. A key innovation toward efficient deep learning reconstruction of a large three-dimensional (3D) event datacube ( $x,y,t$) ( $x,y$, spatial coordinate; $t$, time) is that we decompose the original datacube into massively parallel two-dimensional (2D) imaging subproblems, which are much simpler to solve by a deep neural network. We validated our approach on simulated and experimental data. 

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3. Comparison between shadow imaging and in-line holography for measuring droplet size distributions

   https://doi.org/10.1007/s00348-023-03633-8  

   Erinin, Martin A; Néel, Baptiste; Mazzatenta, Megan T; Duncan, James H; Deike, Luc (May 2023, Experiments in Fluids)

   Kähler, C; Longmire, E; Westerweel, J (Ed.)

   Abstract A direct comparison of the droplet size and number measurements using in-line holography and shadow imaging is presented in three dynamically evolving laboratory scale experiments. The two experimental techniques and image processing algorithms used to measure droplet number and radii are described in detail. Droplet radii as low as$$r = 14$$ $r=14$ µm are measured using in-line holography and$$r = 50$$ $r=50$ µm using shadow imaging. The droplet radius measurement error is estimated using a calibration target (reticle) and it was found that the holographic technique is able to measure droplet radii more accurately than shadow imaging for droplets with$$r \le 625$$ $r\le 625$ µm. Using the measurements of droplet number and size we quantitatively cross-validate and assess the accuracy of the two measurement techniques. The droplet size distributions,N(r), are measured in all three experiments and are found to agree well between the two measurement techniques. In one of the laboratory experiments, simultaneous measurements of droplets ($$r \ge 14$$ $r\ge 14$ µm, using holography) and dry aerosols ($$0.07 \lessapprox r \lessapprox 2$$ $0.07⪅r⪅2$ µm, using an scanning mobility particle sizer and$$0.15 \lessapprox r \lessapprox 5$$ $0.15⪅r⪅5$ µm using an optical particle sizer) are reported, one of the first such comparison to the best of our knowledge. The total number and volume of droplets is found to agree well between both techniques in the three experiments. We demonstrate that a relatively simple shadow imaging technique can be just as reliable when compared to a more sophisticated holographic measurement technique over their common droplet radius measurement range. The agreement in results is shown to be valid over a large range of droplet concentrations, which include experiments with relatively sparse droplet concentrations as low as 0.02 droplets per image. Advantages and disadvantages for the two techniques are discussed in the context of our results. The main advantages to in-line holography are the greater accuracy in droplet radius measurement, greater spatial resolution, larger depth of field, and the high repetition rate and short pulse duration of the laser light source. In comparison, the main advantages to shadow imaging are the simpler experimental setup, image processing algorithm, and fewer computer resources necessary for image processing. Droplet statistics like number and size are found to be very reliable between the two methods for large range of droplet densities,$${\mathcal {P}}_{r>50}$$ $P_{r>50}$, ranging from$$10^{-4} \le {\mathcal {P}}_{r>50} \le 10^{-1}$$ ${10}^{-4}\le P_{r>50}\le {10}^{-1}$cm$$^{-3}$$ ${}^{-3}$, when the two techniques are implemented as shown in this paper. 

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4. Sparse decomposition light-field microscopy for high speed imaging of neuronal activity

   https://doi.org/10.1364/OPTICA.392805  

   Yoon, Young-Gyu; Wang, Zeguan; Pak, Nikita; Park, Demian; Dai, Peilun; Kang, Jeong_Seuk; Suk, Ho-Jun; Symvoulidis, Panagiotis; Guner-Ataman, Burcu; Wang, Kai; et al (October 2020, Optica)

   One of the major challenges in large scale optical imaging of neuronal activity is to simultaneously achieve sufficient temporal and spatial resolution across a large volume. Here, we introduce sparse decomposition light-field microscopy (SDLFM), a computational imaging technique based on light-field microscopy (LFM) that takes algorithmic advantage of the high temporal resolution of LFM and the inherent temporal sparsity of spikes to improve effective spatial resolution and signal-to-noise ratios (SNRs). With increased effective spatial resolution and SNRs, neuronal activity at the single-cell level can be recovered over a large volume. We demonstrate the single-cell imaging capability of SDLFM within vivoimaging of neuronal activity of whole brains of larval zebrafish with estimated lateral and axial resolutions of $\sim <\#comment/>3.5\text{µ<\#comment/>}\mathrm{m}$and $\sim <\#comment/>7.4\text{µ<\#comment/>}\mathrm{m}$, respectively, acquired at volumetric imaging rates up to 50 Hz. We also show that SDLFM increases the quality of neural imaging in adult fruit flies. 

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5. Super-resolved multispectral lensless microscopy via angle-tilted, wavelength-multiplexed ptychographic modulation

   https://doi.org/10.1364/OL.394923  

   Song, Pengming; Wang, Ruihai; Zhu, Jiakai; Wang, Tianbo; Bian, Zichao; Zhang, Zibang; Hoshino, Kazunori; Murphy, Michael; Jiang, Shaowei; Guo, Chengfei; et al (June 2020, Optics Letters)

   We report an angle-tilted, wavelength-multiplexed ptychographic modulation approach for multispectral lensless on-chip microscopy. In this approach, we illuminate the specimen with lights at five wavelengths simultaneously. A prism is added at the illumination path for spectral dispersion. Thus, lightwaves at different wavelengths hit the specimen at slightly different incident angles, breaking the ambiguities in mixed-state ptychographic reconstruction. At the detection path, we place a thin diffuser between the specimen and the monochromatic image sensor for encoding the spectral information into 2D intensity measurements. By scanning the sample to different $x-<\#comment/>y$positions, we acquire a sequence of monochromatic images for reconstructing the five complex object profiles at the five wavelengths. An up-sampling procedure is integrated into the recovery process to bypass the resolution limit imposed by the imager pixel size. We demonstrate a half-pitch resolution of 0.55 µm using an image sensor with 1.85 µm pixel size. We also demonstrate quantitative and high-quality multispectral reconstructions of stained tissue sections for digital pathology applications. 

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