Incoherent, non-invasive and non-scanning superoscillation-based microscope for super-resolution imaging

2020 ◽  
Vol 463 ◽  
pp. 125445 ◽  
Author(s):  
Qingkun Xie ◽  
Yanru Jiang ◽  
Jian Liang ◽  
Enshi Qu ◽  
Liyong Ren
2021 ◽  
Vol 8 ◽  
Author(s):  
Hao Dong ◽  
Ling-Dong Sun ◽  
Chun-Hua Yan

Super-resolution microscopy offers a non-invasive and real-time tool for probing the subcellular structures and activities on nanometer precision. Exploring adequate luminescent probes is a great concern for acquiring higher-resolution image. Benefiting from the atomic-like transitions among real energy levels, lanthanide-doped upconversion nanoparticles are featured by unique optical properties including excellent photostability, large anti-Stokes shifts, multicolor narrowband emissions, tunable emission lifetimes, etc. The past few years have witnessed the development of upconversion nanoparticles as probes for super-resolution imaging studies. To date, the optimal resolution reached 28 nm (λ/36) for single nanoparticles and 82 nm (λ/12) for cytoskeleton structures with upconversion nanoparticles. Compared with conventional probes such as organic dyes and quantum dots, upconversion nanoparticle-related super-resolution microscopy is still in the preliminary stage, and both opportunities and challenges exist. In this perspective article, we summarized the recent advances of upconversion nanoparticles for super-resolution microscopy and projected the future directions of this emerging field. This perspective article should be enlightening for designing efficient upconversion nanoprobes for super-resolution imaging and promote the development of upconversion nanoprobes for cell biology applications.


2020 ◽  
Author(s):  
Dong Wang ◽  
Sujit Sahoo ◽  
Xiangwen Zhu ◽  
Giorgio Adamo ◽  
Cuong Dang

Abstract Super-resolution imaging has been revolutionizing technical analysis in various fields from biological to physical sciences. However, many objects are hidden by strongly scattering media such as biological tissues that scramble light paths, create speckle patterns and hinder object’s visualization, let alone super-resolution imaging. Here, we demonstrate non-invasive super-resolution imaging through scattering media based on a stochastic optical scattering localization imaging (SOSLI) technique. After capturing multiple speckle patterns of photo-switchable emitters, our stochastic approach utilizes the speckle correlation property of scattering media to retrieve an image with 100-nm resolution, eight-fold enhancement compared to the diffraction limit. More importantly, we demonstrate our SOSLI to do non-invasive super-resolution imaging through not only static scattering media, but also dynamic scattering media with strong decorrelation such as biological tissues. Our approach paves the way to non-invasively visualize various samples behind scattering media at unprecedented levels of detail.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Dong Wang ◽  
Sujit K. Sahoo ◽  
Xiangwen Zhu ◽  
Giorgio Adamo ◽  
Cuong Dang

AbstractSuper-resolution imaging has been revolutionizing technical analysis in various fields from biological to physical sciences. However, many objects are hidden by strongly scattering media such as biological tissues that scramble light paths, create speckle patterns and hinder object’s visualization, let alone super-resolution imaging. Here, we demonstrate non-invasive super-resolution imaging through scattering media based on a stochastic optical scattering localization imaging (SOSLI) technique. After capturing multiple speckle patterns of photo-switchable point sources, our computational approach utilizes the speckle correlation property of scattering media to retrieve an image with a 100-nm resolution, an eight-fold enhancement compared to the diffraction limit. More importantly, we demonstrate our SOSLI to do non-invasive super-resolution imaging through not only static scattering media, but also dynamic scattering media with strong decorrelation such as biological tissues. Our approach paves the way to non-invasively visualize various samples behind scattering media at nanometer levels of detail.


2021 ◽  
Vol 13 (10) ◽  
pp. 1956
Author(s):  
Jingyu Cong ◽  
Xianpeng Wang ◽  
Xiang Lan ◽  
Mengxing Huang ◽  
Liangtian Wan

The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework.


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