We propose a novel single-plane phase retrieval method to realize high-quality sample reconstruction for lensfree on-chip microscopy. In our method, complex wavefield reconstruction is modeled as a quadratic minimization problem, where total variation and joint denoising regularization are designed to keep a balance of artifact removal and resolution enhancement. In experiment, we built a 3D-printed field-portable platform to validate the imaging performance of our method, where resolution chart, dynamic target, transparent cell, polystyrene beads, and stained tissue sections are employed for the imaging test. Compared to state-of-the-art methods, our method eliminates image degradation and obtains a higher imaging resolution. Different from multi-wavelength or multi-height phase retrieval methods, our method only utilizes a single-frame intensity data record to accomplish high-fidelity reconstruction of different samples, which contributes a simple, robust, and data-efficient solution to design a resource-limited lensfree on-chip microscope. We believe that it will become a useful tool for telemedicine and point-of-care application.

Facing the demand of industry for metal surface detection, this article addresses the segmentation of metal surface defects. Metal surface defect segmentation has many technical problems, such as serious imbalance in quantity and distribution, low contrast, and weak boundary information. To solve these problems, this article proposes a multi-scale attention feature fusion module, normalized mean square frequency category weight strategy, biased weight training sampling strategy, and category-boundary loss calculation strategy. The multi-scale attention feature fusion module fuses different levels of feature information to obtain defect features in images more effectively. The normalized mean square frequency category weight strategy suppresses the excessive weight of small defect categories and false positive classification. The biased weight training sampling strategy allocates different training frequencies to training samples and increases the attention to defect samples. The category-boundary loss calculation strategy imposes weights on the defect boundary and increases the attention to the boundary information. On four public datasets (Severstal, SD900, Magnetic-tile, and Crack), the proposed method achieves improvements of 7.8%, 4.0%, 18.4%, and 9.4% (absolute quantity), respectively, compared with the baseline method, while the calculation amount and number of parameters remain almost unchanged. Hence, our method ranks as state of the art.

Confocal microscopy is an essential component of wafer defect detection systems. Wafers are raw materials used in the manufacture of semiconductor chips. The semiconductor chip manufacturing process undergoes frequent updates, which cause an increase in the number and types of defects. This leads to lengthy scanning times for large wafers, and warrants the need to enhance the throughput of optical microscopy inspections. To address this issue, we propose the use of the multi-scale residual dilated convolution attention mechanism network (MRDCAN) super-resolution reconstruction algorithm to reproduce high-resolution images from low-magnification objective lens acquired images. The algorithm introduces the attention mechanism to enhance the information richness of wafer images, introduces the multi-scale expansion convolution to expand the convolutional sensor field to eliminate artefacts to enrich the detailed information of wafer image contours, and meets the image quality requirements through the loss calculation method based on the combination of mean-square error (MSE) and structural similarity (SSIM) image evaluation indices. It is shown that the reconstruction of low-resolution wafer images using this algorithm breaks the optical diffraction limit and achieves the purpose of improving the wafer image resolution. Compared with state-of-the-art models, the proposed algorithm can achieve the best performance with an SSIM index of 94.26 percent for the reconstructed super-resolution wafer images. Our algorithm provides fresh insights into the current challenges of confocal microscopy in the field of wafer defect detection Note to Practitioners —Shrinking semiconductor wafer sizes and increasingly complex inspection steps lead to reduced throughput of optical microscope inspection systems. Current convolutional neural network (CNN) networks cannot solve the problem of super-resolution of complex wafer images well. This seriously affects their application in practical detection. Compared with other algorithms, the super-resolution reconstruction algorithm proposed in this paper has a short training time and a multi-scale structure that effectively prevents the loss function curve from oscillating. And the reconstructed wafer image achieves obvious advantages in terms of visual effect and evaluation indices, with strong robustness to Gaussian noise. In addition, the final discussion shows that high-resolution images can be reproduced through the combination of low-magnification objective lens and deep learning super-resolution algorithm, which can simplify the steps of wafer defect detection and increase the efficiency of the whole wafer defect detection by more than 100%. This study demonstrates the potential of super-resolution confocal microscopy for wafer defect detection.

Image scanning microscopy (ISM) is a promising tool for bioimaging owing to its integration of signal to noise ratio (SNR) and super resolution superior to that obtained in confocal scanning microscopy. In this paper, we introduce the annular radially polarized beam to the ISM, which yields an axially extended excitation focus and enhanced resolution, providing a new possibility to obtain the whole information of thick specimen with a single scan. We present the basic principle and a rigorous theoretical model for ISM with annular radially polarized beam (ISM-aRP). Results show that the resolution of ISM-aRP can be enhanced by 4% compared with that in conventional ISM, and the axial extent of the focus is longer than 6λ. The projected view of the simulated fluorescent beads suspension specimen demonstrates the validity of ISM-aRP to obtain the whole information of volume sample. Moreover, this simple method can be easily integrated into the commercial laser scanning microscopy systems.

Radially polarized field under strong focusing has emerged as a powerful manner for fluorescence microscopy. However, the refractive index (RI) mismatch-induced aberrations seriously degrade imaging performance, especially under high numerical aperture (NA). Traditional adaptive optics (AO) method is limited by its tedious procedure. Here, we present a computational strategy that uses artificial neural networks to correct the aberrations induced by RI mismatch. There are no requirements for expensive hardware and complicated wavefront sensing in our framework when the deep network training is completed. The structural similarity index (SSIM) criteria and spatial frequency spectrum analysis demonstrate that our deep-learning-based method has a better performance compared to the widely used Richardson-Lucy (RL) deconvolution method at different imaging depth on simulation data. Additionally, the generalization of our trained network model is tested on new types of samples that are not present in the training procedure to further evaluate the utility of the network, and the performance is also superior to RL deconvolution.