In digital holography, it is critical to know the distance in order to reconstruct the multi-sectional object. This autofocusing is traditionally solved by reconstructing a stack of in-focus and out-of-focus images and using some focus metric, such as entropy or variance, to calculate the sharpness of each reconstructed image. Then the distance corresponding to the sharpest image is determined as the focal position. This method is effective but computationally demanding and time-consuming. To get an accurate estimation, one has to reconstruct many images. Sometimes after a coarse search, a refinement is needed. To overcome this problem in autofocusing, we propose to use deep learning, i.e., a convolutional neural network (CNN), to solve this problem. Autofocusing is viewed as a classification problem, in which the true distance is transferred as a label. To estimate the distance is equated to labeling a hologram correctly. To train such an algorithm, totally 1000 holograms are captured under the same environment, i.e., exposure time, incident angle, object, except the distance. There are 5 labels corresponding to 5 distances. These data are randomly split into three datasets to train, validate and test a CNN network. Experimental results show that the trained network is capable of predicting the distance without reconstructing or knowing any physical parameters about the setup. The prediction time using this method is far less than traditional autofocusing methods.
As a specific digital holographic microscopy system, optical scanning holography (OSH) is an appealing technique that makes use of the advantages of holography in the application of optical microscopy. In OSH system, a three-dimensional object is scanned with a Fresnel zone plate in a raster fashion, and the electrical signals are demodulated into a complex hologram by heterodyne detection. Then the recorded light wavefront information contained in the hologram allows one to digitally reconstruct the specimen for multiple purposes such as optical sectioning, extended focused imaging as well as three-dimensional imaging. According to Abbe criterion, however, akin to those conventional microscopic imaging systems, OSH suffers from limited resolving power due to the finite sizes of the objective lens and the aperture, i.e., low numerical aperture. To bypass the diffraction barrier in light microscopy, various super-resolution imaging techniques have been proposed. Among those methods, structured illumination is an ensemble imaging concept that modulates the spatial frequency by projecting additional well-defined patterns with different orientation and phase shift onto the specimen. Computational algorithms are then applied to remove the effect of the structure and to reconstruct a super-resolved image beyond the diffraction-limit. In this paper, we introduce this technique in OSH system to scale down the spatial resolution beyond the diffraction limit. The performance of the proposed method is validated by simulation and experimental results.
A digital micro-mirror device (DMD) acting as a real-time hologram is an emerging technology in dynamic holographic projection. This paper presents a lensless image magnification method in DMD holography by using a Fresnel hologram. By analyzing the diffraction order distribution in the image plane of a hologram produced by DMD, we find the factors that limit the size of the magnified image. We perform a lensless magnification experiment that shows good magnified images in accordance with the numerical results. Finally, we discuss methods to eliminate longitudinal error and chromatic aberration in three-dimensional (3-D) and color projection, respectively, and present a 3-D image reconstruction result that shows lensless magnification of a 3-D image without distortion. It is believed that this technique can be used in future real-time holographic projection based on digital light processing technology.