Digital slicing of 3D scenes by Fourier filtering of integral images

Improved depth resolution and depth-of-field in temporal integral imaging systems through non-uniform and curved time-lens array

Observing and studying the evolution of rare non-repetitive natural phenomena such as optical rogue waves or dynamic chemical processes in living cells is a crucial necessity for developing science and technologies relating to them. One indispensable technique for investigating these fast evolutions is temporal imaging systems. However, just as conventional spatial imaging systems are incapable of capturing depth information of a three-dimensional scene, typical temporal imaging systems also lack this ability to retrieve depth information-different dispersions in a complex pulse. Therefore, enabling temporal imaging systems to provide these information with great detail would add a new facet to the analysis of ultra-fast pulses. In this paper, after discussing how spatial three-dimensional integral imaging could be generalized to the time domain, two distinct methods have been proposed in order to compensate for its shortcomings such as relatively low depth resolution and limited depth-of-field. The first method utilizes a curved time-lens array instead of a flat one, which leads to an improved viewing zone and depth resolution, simultaneously. The second one which widens the depth-of-field is based on the non-uniformity of focal lengths of time-lenses in the time-lens array. It has been shown that compared with conventional setup for temporal integral imaging, depth resolution, i.e. dispersion resolvability, and depth-of-field, i.e. the range of resolvable dispersions, have been improved by a factor of 2.5 and 1.87, respectively.

Human detection in occluded scenes through optically inspired multi-camera image fusion

In this paper, a novel approach for foreground extraction has been proposed based on a popular three-dimensional imaging technique in optics, called integral imaging. In this approach, multiple viewpoint images captured from a three-dimensional scene are used to extract range information of the scene and effectively extract an object or a person, even in the presence of heavy occlusion. The algorithm consists of two parts: depth estimation and reconstruction of the targeted object at the estimated depth distance. Further processing of the resulting reconstructed image can lead to the detection of a face or a pedestrian in the scene, which may not otherwise be detectable due to partial occlusion in each of the views. The validity of our approach has been demonstrated by experimental results in different scenarios.

Profile preferentially partial occlusion removal for three-dimensional integral imaging

A profile preferentially partial occlusion removal method for integral imaging is presented. The profile of the occlusion always contains details with significant texture structure, and regions with significant texture structure often lead to reliable depth estimation. Taking the advantage of the significant texture structure, the profile of occlusion is preferentially dealt with, and then the entire occlusion region is determined via regional spreading according to the accurate profile. The details of occlusion can be accurately removed and the occluded scene is also retained to the maximum degree. In our method, elemental images are integrated into a four-dimensional light field to provide consistently reliable depth estimation and occlusion decisions among all elemental images. Experimental results show that the proposed method is efficient to deal with the details of the occlusion, and it is robust for the occlusions with different kinds of texture structure.

Three-Dimensional Photon Counting Imaging with Axially Distributed Sensing

In this paper, we review three-dimensional (3D) photon counting imaging with axially distributed sensing. Under severely photon-starved conditions, we have proposed various imaging and algorithmic approaches to reconstruct a scene in 3D, which are not possible by using conventional imaging system due to lack of sufficient number of photons. In this paper, we present an overview of optical sensing and imaging system along with dedicated algorithms for reconstructing 3D scenes by photon counting axially distributed sensing, which may be implemented by moving a single image sensor along its optical axis. To visualize the 3D image, statistical estimation methods and computational reconstruction of axially distributed sensing is applied.

Plenoptic projection fluorescence tomography

A new method to obtain the three-dimensional localization of fluorochrome distributions in micrometric samples is presented. It uses a microlens array coupled to the image port of a standard microscope to obtain tomographic data by a filtered back-projection algorithm. Scanning of the microlens array is proposed to obtain a dense data set for reconstruction. Simulation and experimental results are shown and the implications of this approach in fast 3D imaging are discussed.

Depth extraction by using the correlation of the periodic function with an elemental image in integral imaging

We propose a depth extraction method by using the correlation between an elemental image and a periodic function in computational integral imaging. Because each elemental image corresponds to a different perspective of the three-dimensional (3-D) object, an elemental image is regarded as the sum of the periodic spatial frequencies depending on the depth of a 3-D object. In this regard, we analyze the property of correlation between the same periodic functions and vice versa. To show the feasibility of the proposed method, we carried out our experiment and presented the results.

Iterative reconstruction of projection images from a microlens-based optical detector

A microlens-based optical detector was developed to perform small animal optical imaging. In this paper we present an iterative reconstruction algorithm yielding improved image quality and spatial resolution as compared to conventional inverse mapping. The reconstruction method utilizes the compressive sensing concept to cope with the undersampling nature of the problem. Each iteration in the algorithm contains two separate steps to ensure both the convergence of the least-square solution and the minimization of the l(1)-norm of the sparsifying transform. The results estimated from measurements, employing a Derenzo-like pattern and a Siemens star phantom, illustrate significant improvements in contrast and spatial resolution in comparison to results calculated by inverse mapping.

Artifact analysis and image enhancement in three-dimensional computational integral imaging using smooth windowing technique

We propose an artifact analysis in computational integral imaging and the image enhancement method based on the analysis using the smooth windowing technique. Blurring and lenslet artifacts, which are major problems in computational integral imaging, are defined and analyzed using a signal model. Applying a smooth and continuous window such as the triangular window to computational integral imaging reconstruction provides a dramatic improvement in terms of image quality. Experimental results are presented to show the validity of our method. To our best knowledge, this is the first trial to control a window function in computational integral imaging.

Weak beacon detection for air-to-ground optical wireless link establishment

In an air-to-ground free-space optical communication system, strong background interference seriously affects the beacon detection, which makes it difficult to establish the optical link. In this paper, we propose a correlation beacon detection scheme under strong background interference conditions. As opposed to traditional beacon detection schemes, the beacon is modulated by an m-sequence at the transmitting terminal with a digital differential matched filter (DDMF) array introduced at the receiving end to detect the modulated beacon. This scheme is capable of suppressing both strong interference and noise by correlation reception of the received image sequence. In addition, the DDMF array enables each pixel of the image sensor to have its own DDMF of the same structure to process its received image sequence in parallel, thus it makes fast beacon detection possible. Theoretical analysis and an outdoor experiment have been demonstrated and show that the proposed scheme can realize fast and effective beacon detection under strong background interference conditions. Consequently, the required beacon transmission power can also be reduced dramatically.

Recent progress in three-dimensional information processing based on integral imaging

Recently developed integral imaging techniques are reviewed. Integral imaging captures and reproduces the light rays from the object space, enabling the acquisition and the display of the three-dimensional information of the object in an efficient way. Continuous effort on integral imaging has been improving the performance of the capture and display process in various aspects, including distortion, resolution, viewing angle, and depth range. Digital data processing of the captured light rays can now visualize the three-dimensional structure of the object with a high degree of freedom and enhanced quality. This recent progress is of high interest for both industrial applications and academic research.

3D imaging with axially distributed sensing

A new (to our knowledge) multiperspective 3D imaging architecture is proposed that uses imagers distributed along a common optical axis. In this axially distributed sensing method, either a single imager is translated along its optical axis or objects are moved parallel to the optical axis of a single imager. The 3D information collection capability of the proposed architecture is analyzed and a computational 3D reconstruction algorithm based on ray back-projection is proposed. It is shown analytically and experimentally that the collection capacity of this architecture is not uniform over the field of view. Experimental results are presented to verify the proposed approach. We believe this is the first report on 3D sensing and imaging with axially distributed sensing.

Profilometry and optical slicing by passive three-dimensional imaging

Passive three-dimensional (3D) imaging is an enabling technology for a number of applications. We present a technique for profilometry and optical slicing of objects using 3D multiperspective imaging. We use the spectral radiation pattern (SRP) in object space and establish its relationship to different perspective images. A method is proposed to infer the depth of Lambertian surfaces from the statistics of the SRP. Experimental results are presented to show the feasibility of this method. To the best of our knowledge, this is the first time that statistics of the ray intensity-angle is used for 3D depth mapping.