Ryoichi Horisaki

Irregular Lens Arrangement Design to Improve Imaging Performance of Compound-Eye Imaging Systems

Typical compound-eye imaging systems consist of multiple lenslets arranged regularly because of the advantages of these arrangements, such as simplicity in design, fabrication, and data processing. Such a regular arrangement, however, exhibits strong fluctuation of the imaging performance over the object distance. To solve this problem, irregularity is introduced, and this approach is applied to a compound-eye imaging system called thin observation module by bound optics (TOMBO). An efficient design method for improving the imaging performance is presented. Simulation results, including the peak signal-to-noise ratio of the super-resolved images, confirm the effectiveness of the proposed method.

Superposition Imaging for Three-Dimensionally Space-Invariant Point Spread Functions

In this paper, we propose an aberration compensation method using superposition imaging and inexpensive postprocessing. In the method, the focusing distance and optical axis position of an imaging system with aberrations are varied over certain ranges, and the resulting images are superposed to equalize the point spread function (PSF) within a three-dimensional region and remove space variance. A sharp image of an object with a large depth-of-field and field-of-view is then reconstructed by deconvolution of the superposed image using the effective three-dimensionally space-invariant PSF. The effectiveness of the proposed method was verified by simulations assuming defocus, the five Seidel aberrations, and vignetting.

Compressive holography of diffuse objects

We propose an estimation-theoretic approach to the inference of an incoherent 3D scattering density from 2D scattered speckle field measurements. The object density is derived from the covariance of the speckle field. The inference is performed by a constrained optimization technique inspired by compressive sensing theory. Experimental results demonstrate and verify the performance of our estimates.

Single-shot phase imaging with a coded aperture

We present a method of quantitatively acquiring a large complex field, containing not only amplitude information but also phase information, based on single-shot phase imaging with a coded aperture (SPICA). In SPICA, the propagating field from an object illuminated by partially coherent visible light is sieved by a coded mask, and the sieved field propagates to an image sensor, where it is captured. The sieved field is recovered from the single captured intensity image via a phase retrieval algorithm with an amplitude support constraint using the mask pattern, and then the objects complex field is reconstructed from the recovered sieved field by an algorithm employing a sparsity constraint based on compressive sensing. The system model and the theoretical bounds of SPICA are derived. We also verified the concept with numerical demonstrations.

Multidimensional imaging using compressive Fresnel holography

We propose a generalized framework for single-shot acquisition of multidimensional objects using compressive Fresnel holography. A multidimensional object with spatial, spectral, and polarimetric information is propagated with the Fresnel diffraction, and the propagated signal of each channel is observed by an image sensor with randomly arranged optical elements for filtering. The object data are reconstructed using a compressive sensing algorithm. This scheme is verified with numerical experiments. The proposed framework can be applied to imageries for spectrum, polarization, and so on.

Feasibility study for compressive multi-dimensional integral imaging.

This paper describes a generalized framework for single-exposure acquisition of multi-dimensional scene information using integral imaging system based on compressive sensing. In the proposed system, a multi-dimensional scene containing a plurality of information such as 3D coordinates, spectral and polarimetric data is captured by integral imaging optics. The image sensor uses pixel-wise filtering elements arranged randomly. The multi-dimensional original object is reconstructed using an algorithm with a sparsity constraint. The proposed system is demonstrated with simulations and feasible optical experiments based on synthetic aperture integral imaging using multi-dimensional objects including 3D coordinates, spectral, and polarimetric information.

Generalized sampling using a compound-eye imaging system for multi-dimensional object acquisition.

In this paper, we propose generalized sampling approaches for measuring a multi-dimensional object using a compact compound-eye imaging system called thin observation module by bound optics (TOMBO). This paper shows the proposed system model, physical examples, and simulations to verify TOMBO imaging using generalized sampling. In the system, an object is modulated and multiplied by a weight distribution with physical coding, and the coded optical signal is integrated on to a detector array. A numerical estimation algorithm employing a sparsity constraint is used for object reconstruction.

Computational superposition compound eye imaging for extended depth-of-field and field-of-view

This paper describes a superposition compound eye imaging system for extending the depth-of-field (DOF) and the field-of-view (FOV) using a spherical array of erect imaging optics and deconvolution processing. This imaging system had a three-dimensionally space-invariant point spread function generated by the superposition optics. A sharp image with a deep DOF and a wide FOV could be reconstructed by deconvolution processing with a single filter from a single captured image. The properties of the proposed system were confirmed by ray-trace simulations.

A compound-eye imaging system with irregular lens-array arrangement

TOMBO (Thin Observation Module by Bound Optics) is a compound-eye imaging system inspired by a visual organ of insects. TOMBO has various advantages over conventional imaging systems. However, to demonstrate applicability of TOMBO as an imaging system, high-resolution imaging is significant and unavoidable. In this study, a TOMBO system with irregular lens-array arrangement is proposed and a high-resolution imaging method integrating a super-resolution process with depth acquisition of three-dimensional objects is presented. The proposed TOMBO system improves image resolution for far objects, because it can alleviate degeneration of the sampling points on the far objects caused by the regular arrangement of the lens array in the conventional TOMBO system. An experimental TOMBO has 1.3 mm focal length of lens, 0.5 mm pitch of lenses, 0.5 mm diameter of aperture, 3 × 3 of units, 160 × 160 pixels per unit, and 3.125 μm pitch of pixel. The target planar object is located at 5 m from the TOMBO system. The simulation result shows that the coverage ratio of the sampling points, PSNR of the super-resolved image, and the error of the depth estimation for the object are improved by 50%, 3 dB, and 56%, respectively. The experimental result shows that the error of depth estimation for the planar object located at 3.2 m is 18% and that the contrast of 123 lp/mm at the center of a unit is improved by 0.38 with the super-resolution processing.

Learning-based imaging through scattering media.

We present a machine-learning-based method for single-shot imaging through scattering media. The inverse scattering process was calculated based on a nonlinear regression algorithm by learning a number of training object-speckle pairs. In the experimental demonstration, multilayer phase objects between scattering plates were reconstructed from intensity measurements. Our approach enables model-free sensing, where it is not necessary to know the sensing processes/models.