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

Computational Integral Imaging Reconstruction via Elemental Image Blending without Normalization

This paper presents a novel computational integral imaging reconstruction (CIIR) method using elemental image blending to eliminate the normalization process in CIIR. Normalization is commonly used in CIIR to address uneven overlapping artifacts. By incorporating elemental image blending, we remove the normalization step in CIIR, leading to decreased memory consumption and computational time compared to those of existing techniques. We conducted a theoretical analysis of the impact of elemental image blending on a CIIR method using windowing techniques, and the results showed that the proposed method is superior to the standard CIIR method in terms of image quality. We also performed computer simulations and optical experiments to evaluate the proposed method. The experimental results showed that the proposed method enhances the image quality over that of the standard CIIR method, while also reducing memory usage and processing time.

Image Enhancement for Computational Integral Imaging Reconstruction via Four-Dimensional Image Structure

This paper describes the image enhancement of a computational integral imaging reconstruction method via reconstructing a four-dimensional (4-D) image structure. A computational reconstruction method for high-resolution three-dimensional (3-D) images is highly required in 3-D applications such as 3-D visualization and 3-D object recognition. To improve the visual quality of reconstructed images, we introduce an adjustable parameter to produce a group of 3-D images from a single elemental image array. The adjustable parameter controls overlapping in back projection with a transformation of cropping and translating elemental images. It turns out that the new parameter is an independent parameter from the reconstruction position to reconstruct a 4-D image structure with four axes of x, y, z, and k. The 4-D image structure of the proposed method provides more visual information than existing methods. Computer simulations and optical experiments are carried out to show the feasibility of the proposed method. The results indicate that our method enhances the image quality of 3-D images by providing a 4-D image structure with the adjustable parameter.

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.

Resolution improvements in integral microscopy with Fourier plane recording

Integral microscopes (IMic) have been recently developed in order to capture the spatial and the angular information of 3D microscopic samples with a single exposure. Computational post-processing of this information permits to carry out a 3D reconstruction of the sample. By applying conventional algorithms, both depth and also view reconstructions are possible. However, the main drawback of IMic is that the resolution of the reconstructed images is low and axially heterogeneous. In this paper, we propose a new configuration of the IMic by placing the lens array not at the image plane, but at the pupil (or Fourier) plane of the microscope objective. With this novel system, the spatial resolution is increased by factor 1.4, and the depth of field is substantially enlarged. Our experiments show the feasibility of the proposed method.

Optical full-depth refocusing of 3-D objects based on subdivided-elemental images and local periodic δ-functions in integral imaging

We propose a new approach for optical refocusing of three-dimensional (3-D) objects on their real depth without a pickup-range limitation based on subdivided-elemental image arrays (sub-EIAs) and local periodic δ-function arrays (L-PDFAs). The captured EIA from the 3-D objects locating out of the pickup-range, is divided into a number of sub-EIAs depending on the object distance from the lens array. Then, by convolving these sub-EIAs with each L-PDFA whose spatial period corresponds to the specific object's depth, as well as whose size is matched to that of the sub-EIA, arrays of spatially-filtered sub-EIAs (SF-sub-EIAs) for each object depth can be uniquely extracted. From these arrays of SF-sub-EIAs, 3-D objects can be optically reconstructed to be refocused on their real depth. Operational principle of the proposed method is analyzed based on ray-optics. In addition, to confirm the feasibility of the proposed method in the practical application, experiments with test objects are carried out and the results are comparatively discussed with those of the conventional method.

Free-depths reconstruction with synthetic impulse response in integral imaging

Integral Imaging provides spatial and angular information of three-dimensional (3D) objects, which can be used both for 3D display and for computational post-processing purposes. In order to recover the depth information from an integral image, several algorithms have been developed. In this paper, we propose a new free depth synthesis and reconstruction method based on the two-dimensional (2D) deconvolution between the integral image and a simplified version of the periodic impulse response function (IRF) of the system. The period of the IRF depends directly on the axial position within the object space. Then, we can retrieve the depth information by performing the deconvolution with computed impulse responses with different periods. In addition, alternative reconstructions can be obtained by deconvolving with non-conventional synthetic impulse responses. Our experiments show the feasibility of the proposed method as well as its potential applications.

Optical three-dimensional refocusing from elemental images based on a sifting property of the periodic δ-function array in integral-imaging

We propose a novel approach to optically refocus three-dimensional (3-D) objects on their real depth from the captured elemental image array (EIA) by using a sifting property of the periodic δ-function array (PDFA) in integral-imaging. By convolving the PDFAs whose spatial periods correspond to each object's depth with the sub-image array (SIA) transformed from the EIA, a set of spatially filtered-SIAs (SF-SIAs) for each object's depth can be extracted. These SF-SIAs are then inverse-transformed into the corresponding versions of the EIAs, and from these, 3-D objects with their own perspectives can be reconstructed to be refocused on their depth in the space. The feasibility of the proposed method has been confirmed through optical experiments as well as ray-optical analysis.