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In recent years, Janus microspheres technology has been widely applied and developed in various fields. However, existing studies often rely on electron microscopy images, treating Janus microspheres as spherical to observe their submicron structures. This approach falls short in meeting the demand for precise measurement of the specific position and orientation of microspheres at the single-molecule scale. Here, we propose a standardized processing procedure that integrates multiple image processing algorithms to measure the position of Janus microspheres through the image capture. Our approach enhances the two-dimensional planar positioning accuracy of non-spherical Janus microspheres in micro-nano optical measurement systems. Utilizing this method, we can conduct a further research on the structure and motion characteristics of the microsphere at the single-particle scale. This method has been validated on a transmission illumination optical tweezers platforms, successfully calibrating the central position of the optical trap. Our work offers a greater convenience for measuring non-spherical Janus microspheres in liquid environments. It also provides a fresh perspective for subsequent studies on the motion trajectories and force dynamics of heterogeneous microspheres, showcasing its broad potential for application. • A standardized image processing procedure is proposed. • 2D positioning accuracy for non-spherical Janus microspheres is improved. • Controllable rotation of Janus microspheres with varying sizes and ratios is measured. • Research lays groundwork for Janus microspheres in single-molecule applications. [ABSTRACT FROM AUTHOR]

Fusion of information from infrared and visible images from different modalities has always been a hot research topic in the field of image processing. In order to solve the problem that existing fusion methods cannot fully utilize the deep information of images, this paper proposes a fusion algorithm DAE-Nest based on depth information extraction and enhancement. The algorithm consists of encoder-feature enhancement-fusion layer-decoder modules for unsupervised learning. At first, the improved coding structure is used to extract multi-scale features of the source image; secondly, the extracted features are data enhanced to fully utilize the deep image information; then, the enhanced features are fused according to the corresponding fusion strategy; finally, the decoder takes the input of fused features and reconstructs them into an information-rich fused image. DAE-Nest was subjected to corresponding fusion test experiments on the TNO and RoadScene data sets respectively and compared with 9 excellent fusion algorithms. In terms of subjective evaluation, DAE-Nest can make full use of the deep information in the source image, and the generated fusion image also conforms to the visual perception of the human eyes; in terms of objective evaluation indicators, DAE-Nest's average gradient and spatial frequency on the TNO data set, it achieved the best performance in the three evaluation indicators of structural similarity and structural similarity, and also achieved the best performance in the fusion quality evaluation index on the RoadScene data set. Therefore, the fusion method proposed in this paper has achieved excellent results both subjectively and objectively, and can effectively fuse infrared and visible images, laying a good foundation for subsequent visual tasks. • Design a deep multi-scale feature extraction structure to fully extract the deep scale features of the image. • Design feature enhancement structures to make full use of the deep scale features of the image. • Design end-to-end integration modules IRS (Inception ResNet Shallow) and IRD (Inception ResNet Deep). • The proposed method was evaluated on two test sets and good fusion results were achieved. [ABSTRACT FROM AUTHOR]

Metasurface-based optical differentiators have recently attracted attention for their potential advantages in real-time and high-throughput image processing technology. However, the existing differentiators suffer from limitations in numerical aperture (NA). Here, we propose a differential metasurface based on silicon clusters covered with polydimethylsiloxane, achieving a remarkable NA of 0.7. We numerically demonstrate the proposed differentiator can perform second-order differentiation on a 1D spatial function and high NA can accurately compute smaller-scale variations. We further demonstrate its ability to achieve edge detection on 2D images. Our results advance the development of ultra-high numerical aperture optical differentiator and provide new opportunities for image processing. [ABSTRACT FROM AUTHOR]

Optical differentiation plays a pivotal role in analog computing and image processing, owing to its faster operating speeds and lower power consumption. However, the conventional differentiator encounters a common issue of single and fixed functions due to the lack of effective dynamic modulation means. Here, we proposed a polarization-sensitive differentiator based on a Fano resonance metasurface. The interference between transmitted light and a quasi-guided mode induces Fano resonance, resulting in the leakage of high-frequency component of the p-wave. By adjusting the asymmetry and line width of the Fano resonance line shape, the metasurface can function as a Fourier spatial filter to achieve the differential of an incident beam. Additionally, the direction of the s- and p-wave can be controlled to realize multiple differential functions. We demonstrate the simulation of switchable differential between arbitrary one-dimensional (1D) direction and two-dimensional (2D) directions, with a numerical aperture up to 0.26. It is expected that this switchable optical differentiator will have broad applications in areas such as bioimaging and computer vision. • A dynamic switching between 1D and 2D state differentiator based on an optical metasurface was proposed. • This method requires only a tiny metasurface to achieve an optical differentiator that can dynamically adjust the direction of the differential. • We introduce the s- and p-wave bases and the Fano resonance effect to make the differential function of the metasurface polarization-dependent. • The differentiator shows numerical aperture up to 0.26. [ABSTRACT FROM AUTHOR]