Random phase-free computer-generated hologram

Addition of random phase to the object light is required in computer-generated holograms (CGHs) to widely diffuse the object light and to avoid its concentration on the CGH; however, this addition causes considerable speckle noise in the reconstructed image. For improving the speckle noise problem, techniques such as iterative phase retrieval algorithms and multi-random phase method are used; however, they are time consuming and are of limited effectiveness. Herein, we present a simple and computationally inexpensive method that drastically improves the image quality and reduces the speckle noise by multiplying the object light with the virtual convergence light. Feasibility of the proposed method is shown using simulations and optical reconstructions; moreover, we apply it to lens-less zoom-able holographic projection. The proposed method is useful for the speckle problems in holographic applications

Convolutional neural network-based regression for depth prediction in digital holography

Digital holography enables us to reconstruct objects in three-dimensional space from holograms captured by an imaging device. For the reconstruction, we need to know the depth position of the recoded object in advance. In this study, we propose depth prediction using convolutional neural network (CNN)-based regression. In the previous researches, the depth of an object was estimated through reconstructed images at different depth positions from a hologram using a certain metric that indicates the most focused depth position; however, such a depth search is time-consuming. The CNN of the proposed method can directly predict the depth position with millimeter precision from holograms

Real-time digital holographic microscopy observable in multi-view and multi-resolution

We propose a real-time digital holographic microscopy, that enables simultaneous multiple reconstructed images with arbitrary resolution, depth and positions, using Shifted-Fresnel diffraction instead of Fresnel diffraction. In this system, we used four graphics processing units (GPU) for multiple reconstructions in real-time. We show the demonstration of four reconstruction images from a hologram with arbitrary depths, positions, and resolutions

Handheld and low-cost digital holographic microscopy

This study developed handheld and low-cost digital holographic microscopy (DHM) by adopting an in-line type hologram, a webcam, a high power RGB light emitting diode (LED), and a pinhole. It cost less than 20,000 yen (approximately 250 US dollars at 80 yen/dollar), and was approximately 120 mm x 80 mm x 55 mm in size. In addition, by adjusting the recording-distance of a hologram, the lateral resolution power at the most suitable distance was 17.5 um. Furthermore, this DHM was developed for use in open source libraries, and is therefore low-cost and can be easily developed by anyone. In this research, it is the feature to cut down cost and size and to improve the lateral resolution power further rather than existing reports. This DHM will be a useful application in fieldwork, education, and so forth

Aliasing-reduced Fresnel diffraction with scale and shift operations

Numerical simulation of Fresnel diffraction with fast Fourier transform (FFT) is widely used in optics, especially computer holography. Fresnel diffraction with FFT cannot set different sampling rates between source and destination planes, while shifted-Fresnel diffraction can set different rates. However, an aliasing error may be incurred in shifted-Fresnel diffraction in a short propagation distance, and the aliasing conditions have not been investigated. In this paper, we investigate the aliasing conditions of shifted-Fresnel diffraction and improve its properties based on the conditions

Fast computation of computer-generated hologram using Xeon Phi coprocessor

We report fast computation of computer-generated holograms (CGHs) using Xeon Phi coprocessors, which have massively x86-based processors on one chip, recently released by Intel. CGHs can generate arbitrary light wavefronts, and therefore, are promising technology for many applications: for example, three-dimensional displays, diffractive optical elements, and the generation of arbitrary beams. CGHs incur enormous computational cost. In this paper, we describe the implementations of several CGH generating algorithms on the Xeon Phi, and the comparisons in terms of the performance and the ease of programming between the Xeon Phi, a CPU and graphics processing unit (GPU)

Ptychography by changing the area of probe light and scaled ptychography

Ptychography is a promising phase retrieval technique for visible light, X-ray and electron beams. Conventional ptychography reconstructs the amplitude and phase of an object light from a set of the diffraction intensity patterns obtained by the X-Y moving of the probe light. The X-Y moving of the probe light requires two control parameters and accuracy of the locations. We propose ptychography by changing the area of the probe light using only one control parameter, instead of the X-Y moving of the probe light. The proposed method has faster convergence speed. In addition, we propose scaled ptychography using scaled diffraction calculation in order to magnify retrieved object lights clearly

Calculation reduction method for color computer-generated hologram using color space conversion

We report a calculation reduction method for color computer-generated holograms (CGHs) using color space conversion. Color CGHs are generally calculated on RGB space. In this paper, we calculate color CGHs in other color spaces: for example, YCbCr color space. In YCbCr color space, a RGB image is converted to the luminance component (Y), blue-difference chroma (Cb) and red-difference chroma (Cr) components. In terms of the human eye, although the negligible difference of the luminance component is well-recognized, the difference of the other components is not. In this method, the luminance component is normal sampled and the chroma components are down-sampled. The down-sampling allows us to accelerate the calculation of the color CGHs. We compute diffraction calculations from the components, and then we convert the diffracted results in YCbCr color space to RGB color space

Digital holographic particle volume reconstruction using a deep neural network

This paper proposes a particle volume reconstruction directly from an in-line hologram using a deep neural network. Digital holographic volume reconstruction conventionally uses multiple diffraction calculations to obtain sectional reconstructed images from an in-line hologram, followed by detection of the lateral and axial positions, and the sizes of particles by using focus metrics. However, the axial resolution is limited by the numerical aperture of the optical system, and the processes are time-consuming. The method proposed here can simultaneously detect the lateral and axial positions, and the particle sizes via a deep neural network (DNN). We numerically investigated the performance of the DNN in terms of the errors in the detected positions and sizes. The calculation time is faster than conventional diffracted-based approaches

Gigapixel inline digital holographic microscopy using a consumer scanner

We demonstrate a gigapixel inline digital holographic microscopy using a consumer scanner. The consumer scanner can maximally scan an A4 size image (297mm x 210mm) with 4800 dpi (= 5.29 um), theoretically achieving a resolution of 56,144 x 39,698 = 2.22 gigapixels. The system using a consumer scanner has a simple structure, compared with synthetic aperture digital holography using a camera mounted on a two-dimensional moving stage. In this demonstration, we captured an inline hologram with 23,602 x 18,023 pixels (= 0.43 gigapixels). In addition, to accelerate the reconstruction time of the gigapixel hologram and decrease the amount of memory for the reconstruction, we applied the band-limited double-step Fresnel diffraction to the reconstruction