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.

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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.