Overcoming Abbe's diffraction limit has been a challenging task and one of great interest for biological investigations. The emergence of fluorescence nanoscopy circumvents the diffraction barrier with nearly limitless power for optical microscopy, which enables investigations of the microscopic world in the 1-100 nm range. Proposed variants, such as expansion microscopy (ExM), stimulated emission depletion microscopy (STED), and Airyscan, are innovative in three aspects: sampling, illumination, and detection. These techniques show increasing strength in bioimaging subcellular structures. In this Perspective, we highlight advances in and prospects of fluorescence nanoscopy.
Window detection is a key component in many graphics and vision applications related to 3D city modeling and scene visualization. We present a novel approach for learning to recognize windows in a colored facade image. Rather than predicting bounding boxes or performing facade segmentation, our system locates keypoints of windows, and learns keypoint relationships to group them together into windows. A further module provides extra recognizable information at the window center. Locations and relationships of keypoints are encoded in different types of heatmaps, which are learned in an end-to-end network. We have also constructed a facade dataset with 3 418 annotated images to facilitate research in this field. It has richly varying facade structures, occlusion, lighting conditions, and angle of view. On our dataset, our method achieves precision of 91.4% and recall of 91.0% under 50% IoU (intersection over union). We also make a quantitative comparison with state-of-the-art methods to verify the utility of our proposed method. Applications based on our window detector are also demonstrated, such as window blending.
For decades, the spatial resolution of conventional far‐field optical imaging has been constrained due to the diffraction limit. The emergence of optical super‐resolution imaging has facilitated biological research in the nanoscale regime. However, the existing super‐resolution modalities are not feasible in many biological applications due to weaknesses, like complex implementation and high cost. Recently, various newly proposed techniques are advantageous in the enhancement of the system resolution, background suppression, and improvement of the hardware complexity so that the above‐mentioned issues could be addressed. Most of these techniques entail the modification of factors, like hardware, light path, fluorescent probe, and algorithm, based on conventional imaging systems. Particularly, subtraction technique is an easily implemented, cost‐effective, and flexible imaging tool which has been applied in widespread utilizations. In this review, the principles, characteristics, advances, and biological applications of these techniques are highlighted in optical super‐resolution modalities.
Super-resolution microscopy enables images to be obtained at a resolution higher than that imposed by the diffraction limit of light. Structured illumination microscopy (SIM) is among the fastest super-resolution microscopy techniques currently in use, and it has gained popularity in the field of cytobiology research owing to its low photo-toxicity and widefield modality. In typical SIM, a fluorescent sample is excited by sinusoidal patterns by employing a linear strategy to reconstruct super-resolution images. However, this strategy fails in cases where non-sinusoidal illumination patterns are used. In this study, we propose the least-squares SIM (LSQ-SIM) approach, which is an efficient super-resolution reconstruction algorithm in the framework of least-squares regression that can process raw SIM data under both sinusoidal and non-sinusoidal illuminations. The results obtained in this study indicate the potential of LSQ-SIM for use in structured illumination microscopy and its various application fields.
We present a method for human pose tracking that is based on learning spatiotemporal relationships among joints. Beyond generating the heatmap of a joint in a given frame, our system also learns to predict the offset of the joint from a neighboring joint in the frame. Additionally, it is trained to predict the displacement of the joint from its position in the previous frame, in a manner that can account for possibly changing joint appearance, unlike optical flow. These relational cues in the spatial domain and temporal domain are inferred in a robust manner by attending only to relevant areas in the video frames. By explicitly learning and exploiting these joint relationships, our system achieves state-of-the-art performance on standard benchmarks for various pose tracking tasks including 3D body pose tracking in RGB video, 3D hand pose tracking in depth sequences, and 3D hand gesture tracking in RGB video.
The orientation of a single molecule provides valuable information on fundamental biological processes. We report a technique for the simultaneous estimation of single-molecule 2D position and 2D orientation with ultra-high localization precision (∼2-nm precision with ∼500 photons under a typical 100-nm diameter of excitation beam pattern), which is also compatible with tracking in living cells. In the proposed method, the theoretical precision limits are calculated, and the localization and orientation performance along with potential applications are explored using numerical simulations. Compared to other camera-based orientation measurement methods, it is confirmed that the proposed method can obtain reasonable estimates even under very weak signals (∼15 photons). Moreover, the maximum likelihood estimator (MLE) is found to converge to the theoretical limit when the total number of photons is less than 100.
As one of the mainstream methods of super‐resolution microscopy, stimulated emission depletion (STED) microscopy can effectively break through the diffraction limit of traditional far‐field optical microscopy and increase the resolution of the microscope to the level of nanometre. In the course of about 20 years, STED microscopy has continued to get evolved and has been widely used in many fields, such as life science and physical research, which effectively broadens the scope of our understanding of the microworld. We believe this review will provide a guide for broad readers in related technology. Key Concepts STED microscopy can achieve three‐dimensional super‐resolution imaging with a common resolution of 10–70 nm. Parallelisation illumination can achieve high‐speed imaging using multiple excitation foci to illuminate the specimens. Conventional STED imaging in deep tissue remains challenging mostly due to the fact that light gets scattered in tissues. Under ultrahigh depletion radiation, background signals are generated by secondary excitation of the depletion illumination which has so far prohibited STED microscopy from reaching its theoretically molecular resolution. In practice, the attainment of a higher resolution is notably retarded by the existence of photobleaching effect, a kind of photochemical reaction that permanently inhibits fluorophore molecules to fluoresce.
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