Double-sided computer-generated holography.

We present a method for computer-generated holography (CGH) in which different images are reproduced on both sides of a hologram with a single illumination source. In the proposed method, we use a transmissive spatial light modulator (SLM) and a half mirror (HM) located downstream of the SLM. The light modulated by the SLM is partially reflected by the HM, and the reflected light is modulated again by the SLM for the double-sided image reproduction. We derive an algorithm for double-sided CGH and experimentally demonstrate it.

Single-shot blind deconvolution with coded aperture.

In this paper, we present a method for single-shot blind deconvolution incorporating a coded aperture (CA). In this method, we utilize the CA, inserted on the pupil plane, as support constraints in blind deconvolution. Not only an object is estimated, but also a point spread function of turbulence from a single captured image by a reconstruction algorithm with CA support. The proposed method is demonstrated by simulation and an experiment in which point sources are recovered under severe turbulence.

Incoherent optical phase conjugation.

Optical phase conjugation is a known technique for optically reproducing an object behind a scattering medium. Here we present digital optical phase conjugation through scattering media with spatially and temporally incoherent light. This enables us to eliminate the inevitable light coherence and the need for interferometric measurement for optical phase conjugation. Moreover, we show a method for suppressing background noise, which is critical in incoherent optical phase conjugation. We numerically and experimentally demonstrate the proposed method with background suppression.

Compressive propagation with coherence.

In this Letter, we present wave propagation models of spatially partially coherent (or spatially incoherent) light to compress the computational load of forward and back propagations in inverse problems. In our model, partially coherent light is approximated as a set of random or plane wavefronts passing through spatial bandpass filters, which corresponds to an illumination pupil, and each wave coherently propagates onto a sensor plane through object space. We show that our models reduce the number of coherent propagations in inverse problems, which are essential in optical control and sensing, such as computer-generated holography (CGH) and quantitative phase imaging. We verify the proposed models by numerical and experimental demonstrations of CGH incorporating spatially partially coherent light.

Spectral speckle-correlation imaging.

We present a method for single-shot spectrally resolved imaging through scattering media by using the spectral memory effect of speckles. In our method, a single speckle pattern from a multi-colored object is captured through scattering media with a monochrome image sensor. The color object is recovered by correlation of the captured speckle and a three-dimensional phase retrieval process. The proposed method was experimentally demonstrated by using point sources with different emission spectra located between diffusers. This study paves the way for non-invasive and low-cost spectral imaging through scattering media.

Three-dimensional deeply generated holography [Invited].

In this paper, we present a noniterative method for 3D computer-generated holography based on deep learning. A convolutional neural network is adapted for directly generating a hologram to reproduce a 3D intensity pattern in a given class. We experimentally demonstrated the proposed method with optical reproductions of multiple layers based on phase-only Fourier holography. Our method is noniterative, but it achieves a reproduction quality comparable with that of iterative methods for a given class.

Deep learning wavefront sensing.

We present a new class of wavefront sensors by extending their design space based on machine learning. This approach simplifies both the optical hardware and image processing in wavefront sensing. We experimentally demonstrated a variety of image-based wavefront sensing architectures that can directly estimate Zernike coefficients of aberrated wavefronts from a single intensity image by using a convolutional neural network. We also demonstrated that the proposed deep learning wavefront sensor can be trained to estimate wavefront aberrations stimulated by a point source and even extended sources.