Optimal trap velocity in a dynamic holographic optical trap using a nematic liquid crystal spatial light modulator

A dynamic holographic optical trap uses a dynamic diffractive optical element such as a liquid crystal spatial light modulator to realize one or more optical traps with independent controls. Such holographic optical traps provide a number of flexibilities and conveniences useful in various applications. One key requirement for such a trap is the ability to move the trapped microscopic object from one point to the other with the optimal velocity. In this paper we develop a nematic liquid crystal spatial light modulator based holographic optical trap and experimentally investigate the optimal velocity feasible for trapped beads of different sizes, in such a trap. Our results show that the achievable velocity of the trapped bead is a function of size of the bead, step size, interval between two steps and power carried by the laser beam. We observe that the refresh rate of a nematic liquid crystal spatial light modulator is sufficient to achieve an optimal velocity approaching the theoretical limit in the respective holographic trap for beads with radius larger than the wavelength of light.

Microscopic Object Classification through Passive Motion Observations with Holographic Microscopy

Digital holographic microscopy provides the ability to observe throughout a volume that is large compared to its resolution without the need to actively refocus to capture the entire volume. This enables simultaneous observations of large numbers of small objects within such a volume. We have constructed a microscope that can observe a volume of 0.4 µm × 0.4 µm × 1.0 µm with submicrometer resolution (in xy) and 2 µm resolution (in z) for observation of microorganisms and minerals in liquid environments on Earth and on potential planetary missions. Because environmental samples are likely to contain mixtures of inorganics and microorganisms of comparable sizes near the resolution limit of the instrument, discrimination between living and non-living objects may be difficult. The active motion of motile organisms can be used to readily distinguish them from non-motile objects (live or inorganic), but additional methods are required to distinguish non-motile organisms and inorganic objects that are of comparable size but different composition and structure. We demonstrate the use of passive motion to make this discrimination by evaluating diffusion and buoyancy characteristics of cells, styrene beads, alumina particles, and gas-filled vesicles of micron scale in the field of view.

Super-Resolution Enhancement Method Based on Generative Adversarial Network for Integral Imaging Microscopy

The integral imaging microscopy system provides a three-dimensional visualization of a microscopic object. However, it has a low-resolution problem due to the fundamental limitation of the F-number (the aperture stops) by using micro lens array (MLA) and a poor illumination environment. In this paper, a generative adversarial network (GAN)-based super-resolution algorithm is proposed to enhance the resolution where the directional view image is directly fed as input. In a GAN network, the generator regresses the high-resolution output from the low-resolution input image, whereas the discriminator distinguishes between the original and generated image. In the generator part, we use consecutive residual blocks with the content loss to retrieve the photo-realistic original image. It can restore the edges and enhance the resolution by ×2, ×4, and even ×8 times without seriously hampering the image quality. The model is tested with a variety of low-resolution microscopic sample images and successfully generates high-resolution directional view images with better illumination. The quantitative analysis shows that the proposed model performs better for microscopic images than the existing algorithms.

Microscopic Object Recognition and Localization Based on Multi-Feature Fusion for In-Situ Measurement In Vivo

Microscopic object recognition and analysis is very important in micromanipulation. Micromanipulation has been extensively used in many fields, e.g., micro-assembly operation, microsurgery, agriculture, and biological research. Conducting micro-object recognition in the in-situ measurement of tissue, e.g., in the ion flux measurement by moving an ion-selective microelectrode (ISME), is a complex problem. For living tissues growing at a rate, it remains a challenge to accurately recognize and locate an ISME to protect living tissues and to prevent an ISME from being damaged. Thus, we proposed a robust and fast recognition method based on local binary pattern (LBP) and Haar-like features fusion by training a cascade of classifiers using the gentle AdaBoost algorithm to recognize microscopic objects. Then, we could locate the electrode tip from the background with strong noise by using the Hough transform and edge extraction with an improved contour detection method. Finally, the method could be used to automatically and accurately calculate the relative distance between the two micro-objects in the microscopic image. The results show that the proposed method can achieve good performance in micro-object recognition with a recognition rate up to 99.14% and a tip recognition speed up to 14 frames/s at a resolution of 1360 × 1024. The max error of tip positioning is 6.10 μm, which meets the design requirements of the ISME system. Furthermore, this study provides an effective visual guidance method for micromanipulation, which can facilitate automated micromanipulation research.