Full-color optically-sectioned imaging by wide-field microscopy via deep-learning.

Wide-field microscopy (WFM) is broadly used in experimental studies of biological specimens. However, combining the out-of-focus signals with the in-focus plane reduces the signal-to-noise ratio (SNR) and axial resolution of the image. Therefore, structured illumination microscopy (SIM) with white light illumination has been used to obtain full-color 3D images, which can capture high SNR optically-sectioned images with improved axial resolution and natural specimen colors. Nevertheless, this full-color SIM (FC-SIM) has a data acquisition burden for 3D-image reconstruction with a shortened depth-of-field, especially for thick samples such as insects and large-scale 3D imaging using stitching techniques. In this paper, we propose a deep-learning-based method for full-color WFM, i.e., FC-WFM-Deep, which can reconstruct high-quality full-color 3D images with an extended optical sectioning capability directly from the FC-WFM z-stack data. Case studies of different specimens with a specific imaging system are used to illustrate this method. Consequently, the image quality achievable with this FC-WFM-Deep method is comparable to the FC-SIM method in terms of 3D information and spatial resolution, while the reconstruction data size is 21-fold smaller and the in-focus depth is doubled. This technique significantly reduces the 3D data acquisition requirements without losing detail and improves the 3D imaging speed by extracting the optical sectioning in the depth-of-field. This cost-effective and convenient method offers a promising tool to observe high-precision color 3D spatial distributions of biological samples.

Robust contrast-transfer-function phase retrieval via flexible deep learning networks: publisher's note.

This publisher's note contains corrections to Opt. Lett.44, 5141 (2019)OPLEDP0146-959210.1364/OL.44.005141.

Robust contrast-transfer-function phase retrieval via flexible deep learning networks.

By exploiting the total variation (TV) regularization scheme and the contrast transfer function (CTF), a phase map can be retrieved from single-distance coherent diffraction images via the sparsity of the investigated object. However, the CTF-TV phase retrieval algorithm often struggles in the presence of strong noise, since it is based on the traditional compressive sensing optimization problem. Here, convolutional neural networks, a powerful tool from machine learning, are used to regularize the CTF-based phase retrieval problems and improve the recovery performance. This proposed method, the CTF-Deep phase retrieval algorithm, was tested both via simulations and experiments. The results show that it is robust to noise and fast enough for high-resolution applications, such as in optical, x-ray, or terahertz imaging.

Multi-view object topography measurement with optical sectioning structured illumination microscopy.

Various optical instruments have been developed for three-dimensional (3D) surface topography, including the white light interference, reflectance confocal microscopes, and digital holographic microscopes, etc. However, the steep local slope of objects may cause the light to be reflected in a way that it will not be captured by the objective lens because of the finite collection angle of the objective. To solve this "shadow problem," we report a method to enlarge the collection angle range of optical sectioning structured illumination microscopy by capturing sectioned images of the objects from multiple angle of views. We develop a multi-view image fusion algorithm to reconstruct a single 3D image. Using this method, we detect previously invisible details whose slopes are beyond the collection angle of the objective. The proposed approach is useful for height map measurement and quantitative analyses in a variety of fields, such as biology, materials science, microelectronics, etc.

Large-scale 3D imaging of insects with natural color.

High-resolution 3D imaging technology has found a number of applications in many biological fields. However, the existing 3D imaging tools are often too time-consuming to use on large-scale specimens, such as centimeter-sized insects. In addition, most 3D imaging systems discard the natural color information of the specimens. To surmount these limitations, we present a structured illumination-based approach capable of delivering large field-of-view three-dimensional images. With this approach, 580nm lateral resolution full-color 3D images and 3D morphological data in the size range of typical insect samples can be obtained. This method provides a promising approach that can be used to support many different types of entomological investigations, including taxonomy, evolution, bionics, developmental biology, functional morphology, paleontology, forestry, etc.