High-quality Super-resolution Images Research Articles

Single image super-resolution under multi-frame method

The multi-frame image super-resolution method utilizes a series of low-resolution images from the same scene to reconstruct the corresponding high-quality super-resolution image. However, the insufficient input low-resolution images make it incapable of reconstructing the high-resolution image from a single low-resolution image. In this work, we extend the framework of multi-frame image super-resolution to handle the single low-resolution image via rolling guidance filtering. And an improved version of diffusion-driven regularizer-based multi-frame image super-resolution algorithm is proposed and applied on passive millimeter-wave (PMMW) image super-resolution. Specifically, the joint filtering is first exploited to suppress the noise of single low-resolution noisy image. The rolling guidance method is exploited to generate the structurally multi-scale low-resolution images forming the basis of multi-frame image super-resolution. The generated image sequences are then fed to the nonlinear diffusion regularizer-based super-resolution algorithm. The two-directional total variation de-convolution is finally employed to remove the blur, producing a sharp and clear high-resolution image. Experiments demonstrate the effectiveness of the proposed method and show its superiority for the natural and PMMW images.

X10 expansion microscopy enables 25-nm resolution on conventional microscopes.

Expansion microscopy is a recently introduced imaging technique that achieves super‐resolution through physically expanding the specimen by ~4×, after embedding into a swellable gel. The resolution attained is, correspondingly, approximately fourfold better than the diffraction limit, or ~70 nm. This is a major improvement over conventional microscopy, but still lags behind modern STED or STORM setups, whose resolution can reach 20–30 nm. We addressed this issue here by introducing an improved gel recipe that enables an expansion factor of ~10× in each dimension, which corresponds to an expansion of the sample volume by more than 1,000‐fold. Our protocol, which we termed X10 microscopy, achieves a resolution of 25–30 nm on conventional epifluorescence microscopes. X10 provides multi‐color images similar or even superior to those produced with more challenging methods, such as STED, STORM, and iterative expansion microscopy (iExM). X10 is therefore the cheapest and easiest option for high‐quality super‐resolution imaging currently available. X10 should be usable in any laboratory, irrespective of the machinery owned or of the technical knowledge.

Fast, Background-Free DNA-PAINT Imaging Using FRET-Based Probes

DNA point accumulation in nanoscale topography (DNA-PAINT) enables super-resolution microscopy by harnessing the predictable, transient hybridization between short dye-labeled "imager" and complementary target-bound "docking" strands. DNA-PAINT microscopy allows sub-5 nm spatial resolution, spectrally unlimited multiplexing, and quantitative image analysis. However, these abilities come at the cost of nonfluorogenic imager strands, also emitting fluorescence when not bound to their docking strands. This has thus far prevented rapid image acquisition with DNA-PAINT, as the blinking rate of probes is limited by an upper-bound of imager strand concentrations, which in turn is dictated by the necessity to facilitate the detection of single-molecule binding events over the background of unbound, freely diffusing probes. To overcome this limitation and enable fast, background-free DNA-PAINT microscopy, we here introduce FRET-based imaging probes, alleviating the concentration-limit of imager strands and speeding up image acquisition by several orders of magnitude. We assay two approaches for FRET-based DNA-PAINT (or FRET-PAINT) using either fixed or transient acceptor dyes in combination with transiently binding donor-labeled DNA strands and achieve high-quality super-resolution imaging on DNA origami structures in a few tens of seconds. Finally, we also demonstrate the applicability of FRET-PAINT in a cellular environment by performing super-resolution imaging of microtubules in under 30 s. FRET-PAINT combines the advantages of conventional DNA-PAINT with fast image acquisition times, facilitating the potential study of dynamic processes.

Development of robust neighbor embedding based super-resolution scheme

In this paper, we propose a robust neighbor embedding super-resolution (RNESR) scheme to generate a super-resolution (SR) image from a single low-resolution (LR) image. It utilizes histogram matching for selection of best training pair of images. This helps to learn co-occurrence prior to high-resolution (HR) image reconstruction. The global neighborhood size is computed from local neighborhood size, which avoids the over-fitting and under-fitting problem during neighbor embedding. Robust locally linear embedding (RLLE) is used in place of locally linear embedding (LLE) to generate HR image. To validate the scheme, exhaustive simulation has been carried out on standard images. Comparative analysis with respect to different measures like peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and feature similarity index (FSIM) reveals that the RNESR scheme generates high-quality SR image from a LR image as compared to existing schemes.

Super resolution reconstruction based on block matching and three‐dimensional filtering with sharpening

Super resolution (SR) reconstruction is often considered to be an inverse problem in the sense that unknown high resolution images are sought for giving low resolution images. Recent studies have shown that the sparsity regularisation used in compressed sensing (CS) reconstruction improves the performance of SR reconstruction. Furthermore, under the assumption that mutually similar regions exist within a natural image, non-local (NL) estimation produces accurate estimates for given degraded images. The incorporation of this NL estimation in SR reconstruction has been shown to yield better reconstructions. In this study, the authors propose the use of block matching and three-dimensional filtering with sharpening estimation as the regularisation constraint under the CS-based SR framework. This estimation collects similar blocks and adaptively filters them by the shrinkage of the transform coefficients. It recovers detailed structures while attenuating ringing artefacts. In addition, a sharpening technique used in the estimation also emphasises edges. As a result, the proposed SR algorithm searches for the solution that is similar to this enhanced estimate from among all feasible solutions. The experimental results demonstrate that the proposed method provides high-quality SR images, both numerically and subjectively.

Super-resolution imaging of nuclear bodies by STED microscopy.

The sizes of nuclear bodies and other nuclear structures are normally no more than a few hundred nanometers. This size is below the resolution limit of light microscopy and thus requires electron microscopy for direct observation. Recent developments in super-resolution microscopy have extended the resolution of light microscopy to beyond 100 nm. Here, we describe a super-resolution technique, gated STED, for the analysis of the structure of nuclear bodies, with emphasis on the sample preparation and other technical tips that are important to obtain high-quality super-resolution images.

Three-dimensional super-resolution structured illumination microscopy with maximum a posteriori probability image estimation.

We introduce and demonstrate a new high performance image reconstruction method for super-resolution structured illumination microscopy based on maximum a posteriori probability estimation (MAP-SIM). Imaging performance is demonstrated on a variety of fluorescent samples of different thickness, labeling density and noise levels. The method provides good suppression of out of focus light, improves spatial resolution, and allows reconstruction of both 2D and 3D images of cells even in the case of weak signals. The method can be used to process both optical sectioning and super-resolution structured illumination microscopy data to create high quality super-resolution images.

GraspJ - An Open Source, Real-Time Analysis Package for Super-Resolution Imaging

In the past decade, a number of techniques have been developed that overcome the diffraction limit in fluorescence microscopy. Among these techniques, the ones that are based on single molecule photoactivation and localization, such as STORM, PALM and GSDIM, heavily depend on image analysis for achieving high resolution. With most current analysis software, the post-processing takes orders of magnitude longer than data acquisition. In particular, recent application of these techniques in live-cells came with tremendous advancements in temporal resolution, further increasing the gap between acquisition and analysis speed. To fill this gap we developed an open source, full featured, real-time analysis package (GraspJ - GPU-Run Analysis of STORM and PALM data for ImageJ) that keeps up with the highest possible acquisition speeds of modern EMCCDs. By taking advantage of the parallel processing power of the Graphics Processing Unit (GPU), we were able to achieve more than 1,000,000 molecule fits/second, using a maximum likelihood estimator. Therefore, with GraspJ, analysis can be performed in real-time in parallel to data acquisition, using almost any acquisition software. During real-time analysis, complex post processing (e.g. intrinsic drift correction, trailing of molecule peaks over several frames, high resolution rendering and dynamic zooming) can be easily done without reducing the analysis time significantly. For example, processing times of less than 1ms per frame were achieved using typical imaging conditions (256x256 pixels, ∼50-100 molecules per frame). In addition, GraspJ can be run as an ImageJ plugin, leveraging thousands of features and plugins readily available for ImageJ and therefore providing a convenient and familiar environment. Overall, we believe that the ability to see the drift corrected, high quality super-resolution image building up during acquisition using GraspJ will be of great benefit to the experimenter.

Evaluation of Fluorophores for Optimal Performance in Localization-Based Super-Resolution Imaging

A variety of approaches have recently been developed to surpass the diffraction-limited resolution of fluorescence microscopy. One such approach relies on the sequential activation and nanoscale localization of single fluorophores, where the high-precision localizations of individual molecules can be used to construct a sub-diffraction-limit image with dramatically improved spatial resolutions. Photoswitchable fluorescent probes have become an essential component in localization-based super-resolution imaging. The properties of these probes, such as the photons per switching event, on/off duty cycle, photostability, and number of switching cycles, determine a probe's effectiveness for creating high resolution images. Yet a quantitative characterization of these properties for most fluorophores is lacking, preventing an effective and systematic choice of optimal fluorophores and imaging conditions for attaining high quality super-resolution images. To address this need, we systematically characterize the switching properties of tens of organic dyes and demonstrate how these properties directly impact the quality of a super-resolution image. Our analysis provides a set of guidelines to be followed for evaluating fluorophores and is a resource for probe selection for interested practitioners of single molecule localization-based super-resolution imaging. In addition to our quantitative characterization, we also identify several new dyes suitable for this imaging modality. Using the highest performing dyes from our screen in each of four distinct spectral ranges, we demonstrate low crosstalk, four-color super-resolution imaging and apply these new imaging capabilities to study various cellular structures.

Spherical metallic nanoparticle arrays for super-resolution imaging

We report super-resolution imaging in a metamaterial system comprising spherical silver nanoparticle chain arrays, where each chain consists of nanoparticles with a smaller particle added to the end. Our simulations reveal that silver nanoparticale chains have subwavelength resolution capability at visible wavelengths and that the field intensity in the imaging plane varies with the number of layers of nanoparticles, their polarization, and their coupling. By adding a smaller nanoparticle at the end of each chain, the resolution capability is significantly enhanced, and high-quality super-resolution imaging can be realized for incident waves polarized along the chain direction.