Computational Integral Imaging Reconstruction Method Research Articles

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.

3D view image reconstruction in computational integral imaging using scale invariant feature transform and patch matching.

This paper realizes a computational integral imaging reconstruction method via scale invariant feature transform (SIFT) and patch matching to improve the visual quality of reconstructed 3D view images. To our knowledge, the 3D view images reconstructed from the elemental images suffer from artifacts, which leads to degradations in the visual quality. To prevent image degradation, in this paper, we use the correct regions obtained from the view images taken directly from the original object or use patch matching to replace the distorted regions. However, the initial matching regions could not meet our requirements owing to the limitations of the equipment and the inevitable shortcomings of the experimental operation. To solve these problems, we adopt SIFT descriptors and perspective transform to get the satisfying correct regions. We present the simulation and experimental results of the 3D view images and the evaluation of the quality of the corresponding images to test the performance of the proposed method. The simulation and experimental results indicate that the proposed method can significantly improve the visual quality of the 3D view images and verify the feasibility and effectiveness of the proposed method.

Depth extraction method with high accuracy in integral imaging based on moving array lenslet technique

In order to improve depth extraction accuracy, a method using moving array lenslet technique (MALT) in pickup stage is proposed, which can decrease the depth interval caused by pixelation. In this method, the lenslet array is moved along the horizontal and vertical directions simultaneously for N times in a pitch to get N sets of elemental images. Computational integral imaging reconstruction method for MALT is taken to obtain the slice images of the 3D scene, and the sum modulus (SMD) blur metric is taken on these slice images to achieve the depth information of the 3D scene. Simulation and optical experiments are carried out to verify the feasibility of this method.

Resolution Enhanced Computational Integral Imaging Reconstruction by Using Boundary Folding Mirrors

In this paper, we present a resolution-enhanced computational integral imaging reconstruction method by using boundary folding mirrors. In the proposed method, to improve the resolution of the computationally reconstructed 3D images, the direct and reflected light information of the 3D objects through a lenslet array with boundary folding mirrors is recorded as a combined elemental image array. Then, the ray tracing method is employed to synthesize the regular elemental image array by using a combined elemental image array. From the experimental results, we can verify that the proposed method can improve the visual quality of the computationally reconstructed 3D images.

Image quality improvement in computational reconstruction of partially occluded objects using two computational integral imaging reconstruction methods

In this paper, we propose an image quality improvement method of partially occluded objects using two different computational integral imaging reconstruction (CIIR) methods. In the proposed method, we first remove the occlusion in the recorded elemental images using two different plane images which are generated from two different CIIR methods. We introduce a CIIR method based on a round-mapping model for combined use of the previous method. The difference between two plane images reconstructed at a specific distance enables us to estimate the position of the occlusion in the elemental images. The occlusion-removed elemental images are used to reconstruct the improved 3D images. We carry out some experiments and present the results to show the usefulness of the proposed method.

고속 컴퓨터 집적 영상 복원 방법을 이용한 비선형 3D 영상 상관기

In this paper, we propose a novel nonlinear 3D image correlator using a fast computational integral imaging reconstruction (CIIR) method. In order to implement the fast CIIR method, the magnification process was eliminated. In the proposed correlator, elemental images of the reference and target objects are picked up by lenslet arrays. Using these elemental images, reference and target plane images are reconstructed on the output plane by means of the proposed fast CIIR method. Then, through nonlinear cross-correlations between the reconstructed reference and the target plane images, the pattern recognition can be performed from the correlation outputs. Nonlinear correlation operation can improve the recognition of 3D objects. To show the feasibility of the proposed method, some preliminary experiments are carried out and the results are presented by comparing the conventional method.

Three-dimensional image correlator using fast computational integral imaging reconstruction method based on pixel-to-pixel mapping

In this paper, a three-dimensional (3D) image correlator using a fast computational integral imaging reconstruction (CIIR) method based on a pixel-to-pixel mapping is proposed. In order to implement the fast CIIR method, we replace the magnification process in the conventional CIIR by a pixel-to-pixel mapping. The proposed fast CIIR method reconstructs two sorts of plane images; a plane image whose quality is sufficient, and a dot pattern plane image insufficient to view. This property is very useful to enhance the performance of a CIIR-based image correlator. Thus, we apply the fast CIIR method to a CIIR-based image correlator. To show the feasibility of the proposed method, some preliminary experiments on both pattern correlation and computational cost are carried out, and the results are presented. Our experimental results indicate that the proposed image correlator is superior to the previous method in terms of both correlation performance and complexity.

Image quality enhancement of computational integral imaging reconstruction for partially occluded objects using binary weighting mask on occlusion areas

This paper presents an image quality enhancement of computational integral imaging reconstruction (CIIR) method by using a binary weighting mask on occlusion areas in elemental images. The proposed method utilizes a block-matching algorithm to estimate the occlusion areas in elemental images. Then, a binary weighting mask generated from the estimated occlusion area is applied to our CIIR method. This minimizes the overlapping effect of occluding objects in the reconstructed plane images and thus improves visual quality dramatically. To show the usefulness of our proposed scheme, we conduct several experiments and present the results. The experimental results indicate that our method is superior to the existing methods.

Phase-only hologram generation based on integral imaging and its enhancement in depth resolution

We introduce a phase-only hologram generation method based on an integral imaging, and propose an enhancement method in representable depth interval. The computational integral imaging reconstruction method is modified based on optical °ow to obtain depth-slice images for the focused objects only. A phase-only hologram for multiple plane images is generated using the iterative Fresnel transform algorithm. In addition, a division method in hologram plane is proposed for enhancement in the representable minimum depth interval.

Fast Computational Integral Imaging Reconstruction Method Using a Fractional Delay Filter

In this paper, we propose a fast method for computational integral imaging reconstruction (CIIR) in computational integral imaging (CInIm). The previous CIIR method requires a very high computational cost to reconstruct a volumetric image from elemental images. To overcome this problem, we introduce a fast technique that reduces the computational cost markedly in comparison with the previous method. The proposed method employs a fractional delay (FD) filter in place of the magnification of elemental images. Then, the FD-filtered elemental images are superimposed on each other. We calculate the numbers of pixels that are considered in our CIIR method and in the previous one. On the basis of this, the proposed method can be a fast process because the number of pixels from our method is much less than that of pixels from the previous one. To show the usefulness of the proposed method, we carry out preliminary experiments and present the results in terms of computational cost and visual quality.

Computational integral-imaging reconstruction-based 3-D volumetric target object recognition by using a 3-D reference object

In this paper, we propose a novel computational integral-imaging reconstruction (CIIR)-based three-dimensional (3-D) image correlator system for the recognition of 3-D volumetric objects by employing a 3-D reference object. That is, a number of plane object images (POIs) computationally reconstructed from the 3-D reference object are used for the 3-D volumetric target recognition. In other words, simultaneous 3-D image correlations between two sets of target and reference POIs, which are depth-dependently reconstructed by using the CIIR method, are performed for effective recognition of 3-D volumetric objects in the proposed system. Successful experiments with this CIIR-based 3-D image correlator confirmed the feasibility of the proposed method.

Computational integral imaging reconstruction method of 3D images using pixel-to-pixel mapping and image interpolation

In this paper, we propose a novel computational integral imaging reconstruction (CIIR) method to improve the visual quality of the reconstructed images using a pixel-to-pixel mapping and an interpolation technique. Since an elemental image is magnified inversely through the corresponding pinhole and mapped on the reconstruction output plane based on pinhole-array model in the conventional CIIR method, the visual quality of reconstructed output image (ROI) degrades due to the interference problem between adjacent pixels during the superposition of the magnified elemental images. To avoid this problem, the proposed CIIR method generates dot-pattern ROIs using a pixel-to-pixel mapping and substitutes interpolated values for the empty pixels within the dot-pattern ROIs using an interpolation technique. The interpolated ROIs provides a much improved visual quality compared with the conventional method because of the exact regeneration of pixel positions sampled in the pickup process without interference between pixels. Moreover, it can enable us to reduce a computational cost by eliminating the magnification process used in the conventional CIIR. To confirm the feasibility of the proposed system, some experiments are carried out and the results are presented.

Resolution-enhanced three-dimensional image reconstruction by use of smart pixel mapping in computational integral imaging

Computational integral imaging method can digitally provide a series of plane images of three-dimensional (3D) objects. However, the resolution of 3D reconstructed images is dramatically degraded as the distance from the lenslet array increases. In this paper, to overcome this problem, we propose a novel computational integral imaging reconstruction (CIIR) method based on smart pixel mapping (SPM). Since SPM is a computational process in which elemental images recorded at long distances are convertible to ones recorded near lenslet array, this can give us the improved resolution of plane images for 3D objects located at a long distance range from a lenslet array. For the effective use of the SPM-based CIIR method, we design a novel two-stage CIIR method by the combined use of the conventional CIIR and the SPM-based one. The conventional CIIR method is applied over a short distance range, while the SPM-based CIIR is used over a long distance range. We carry out some experiments to verify the performance of the two-stage CIIR system. From the experimental results, the proposed system can provide a substantial gain over a long distance range in terms of the resolution of reconstructed plane images.

Occlusion removal method of partially occluded 3D object using sub-image block matching in computational integral imaging

In this paper, we propose an occlusion removal method using sub-image block matching for improved recognition of partially occluded 3D objects in computational integral imaging (CII). When 3D plane images are reconstructed in CII, occlusion degrades the resolution of reconstructed images. To overcome this problem, we apply the sub-image transform to elemental image array (EIA) and these sub-images are employed using block matching method for depth estimation. Based on the estimated depth information, we remove the unknown occlusion. After completing the occlusion removal for all sub-images, we obtain the modified EIA without occlusion information through the inverse sub-image transform. Finally, the 3D plane images are reconstructed by using a computational integral imaging reconstruction method with the modified EIA. The proposed method can provide a substantial gain in terms of the visual quality of 3D reconstructed images. To show the usefulness of the proposed method we carry out some experiments and the results are presented.

Improved Viewing Quality of 3-D Images in Computational Integral Imaging Reconstruction Based on Round Mapping Model

In this paper, we propose a computational integral imaging reconstruction (CIIR) method using a round mapping model to improve the viewing quality of 3-D images. The proposed CIIR method can overcome the problem of non-uniformly reconstructed images caused by the conventional method. To show the usefulness of proposed method, some experiments are carried out and the results are presented.

Image quality enhancement in 3D computational integral imaging by use of interpolation methods

In this paper, we propose a computational integral imaging reconstruction (CIIR) method by use of image interpolation algorithms to improve the visual quality of 3D reconstructed images. We investigate the characteristics of the conventional CIIR method along the distance between lenslet and objects. What we observe is that the visual quality of reconstructed images is periodically degraded. The experimentally observed period is half size of the elemental image. To remedy this problem, we focus on the interpolation methods in computational integral imaging. Several interpolation methods are applied to the conventional CIIR method and their performances are analyzed. To objectively evaluate the proposed CIIR method, we introduce an experimental framework for the computational pickup process and the CIIR process using a Gaussian function. We also carry out experiments on real objects to subjectively evaluate the proposed method. Experimental results indicate that our method outperforms the conventional CIIR method. In addition, our method reduces the grid noise that the conventional CIIR method suffers from.

Enhanced-resolution computational integral imaging reconstruction using an intermediate-view reconstruction technique

A new enhanced-resolution computational integral imaging reconstruction method employing an intermediate-view reconstruction (IVR) technique is presented. In the proposed method, as many intermediate elemental images as required can be synthesized from the limited number of picked-up elemental images by using the IVR technique. With sufficient overlapping of this increased number of elemental images in the reconstruction image plane, an enhanced-resolution 3-D image can be displayed. To show the feasibility of the proposed scheme, some experiments were performed, and their results are presented.