Zhenbo Ren

Extended focused imaging and depth map reconstruction in optical scanning holography.

In conventional microscopy, specimens lying within the depth of field are clearly recorded whereas other parts are blurry. Although digital holographic microscopy allows post-processing on holograms to reconstruct multifocus images, it suffers from defocus noise as a traditional microscope in numerical reconstruction. In this paper, we demonstrate a method that can achieve extended focused imaging (EFI) and reconstruct a depth map (DM) of three-dimensional (3D) objects. We first use a depth-from-focus algorithm to create a DM for each pixel based on entropy minimization. Then we show how to achieve EFI of the whole 3D scene computationally. Simulation and experimental results involving objects with multiple axial sections are presented to validate the proposed approach.

Resolution enhancement of optical scanning holography with a spiral modulated point spread function

In optical scanning holography, one pupil produces a spherical wave and another produces a plane wave. They interfere with each other and result in a fringe pattern for scanning a three-dimensional object. The resolution of the hologram reconstruction is affected by the point spread function (PSF) of the optical system. In this paper, we modulate the PSF by a spiral phase plate, which significantly enhances the lateral and depth resolution. We explain the theory for such resolution enhancement and show simulation results to verify the efficacy of the approach.

Analysis of the noise in backprojection light field acquisition and its optimization

Light field reconstruction from images captured by focal plane sweeping can achieve high lateral resolution comparable to the modern camera sensor. This is impossible for the conventional micro-lenslet-based light field capture systems. However, the severe defocus noise and the low depth resolution limit its applications. In this paper, we analyze the defocus noise in the focal-plane-sweeping-based light field reconstruction technique, and propose a method to reduce the defocus noise. Both numerical and experimental results verify the proposed method.

High-resolution Fourier hologram synthesis from photographic images through computing the light field

We present a technique for synthesizing the Fourier hologram of a three-dimensional scene from its light field. The light field captures the volumetric information of an object, and an important advantage is that it does not require coherent illumination, as in conventional holography. In this work, we show how to obtain a high-resolution digital hologram with the light field obtained from a series of photographic images captured along the optical axis. The method is verified both by simulations and experimentally captured light field.

Demonstration of an Autostereoscopic Three-Dimensional Light-Emitting Diode Display Using Diffractive Optical Elements Sheet

A prototype autostereoscopic three-dimensional (3D) light-emitting diode (LED) display using a cylindrical diffractive optical elements (C-DOEs) sheet as the optical steering element is proposed. The operation of the system and the calculation method of the system parameters are described in detail. The DOEs sheet is placed from a distance from the LED display panel which is five times smaller of the existing technology, and the column spacings of the pixels of the LED display panel are nonequal in order to equalize the distance between the viewing zones. The prototype has a 1.33 m2 display panel, 384 ×144 resolution and a horizontal field of view of 60°. The experimental result shows that the proposed method is a potential autostereoscopic technology for the large area LED displays.

Depth Enhancement of Optical Scanning Holography with a Spiral Phase Plate

A spiral phase plate is applied to the optical scanning holography system to improve the depth resolution of the reconstruction, the simulation results show that the depth interval can be resolved at a 0.4 mm with only a single hologram.

Autofocusing of Optical Scanning Holography Based on Entropy Minimization

In optical scanning holography, extracting distance of object is an indispensable step for numerical reconstruction. In this paper, we use entropy as a measurement to achieve autofocusing under different situations.

Magnification of optical image in holography projection using lensless Fresnel holography

Jianshe MaPing SuFeipeng XiaZhenbo RenTong LiuTsinghua UniversityGraduate School at ShenzhenShenzhen, Guangdong 518055, ChinaE-mail: su.ping@mail.sz.tsinghua.edu.cnAbstract. A digital micro-mirror device (DMD) acting as a real-time holo-gram is an emerging technology in dynamic holographic projection. Thispaper presents a lensless image magnification method in DMD hologra-phy by using a Fresnel hologram. By analyzing the diffraction order dis-tribution in the image plane of a hologram produced by DMD, we find thefactors that limit the size of the magnified image. We perform a lenslessmagnification experiment that shows good magnified images in accor-dance with the numerical results. Finally, we discuss methods to eliminatelongitudinal error and chromatic aberration in three-dimensional (3-D) andcolor projection, respectively, and present a 3-D image reconstructionresult that shows lensless magnification of a 3-D image without distortion.Itisbelievedthatthistechniquecanbeusedinfuturereal-timeholographicprojection based on digital light processing technology.

Autofocusing in digital holography using deep learning

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.

Automatic focusing for multisectional objects in digital holography using the structure tensor

Determining the axial position of the recorded object in digital holography is a crucial step for image reconstruction. When multiple discrete sections of a three-dimensional object are overlapping each other, this issue becomes more challenging. In this Letter, an autofocusing algorithm using the structure tensor and its eigenvalues is proposed. This method can extract the focal distance of each section for a multi-sectional object irrespective of whether the sections are overlapping or not. We validate the applicability of the proposed technique with synthesized and experimental data using two types of holographic systems.