Proc. SPIE. 10666, Three-Dimensional Imaging, Visualization, and Display 2018
KEYWORDS: Point spread functions, 3D image enhancement, 3D image reconstruction, Visualization, Image resolution, Convolution, Reconstruction algorithms, Geometrical optics, Integral imaging, 3D image processing
In this paper, we propose a visual quality enhancement of 3D reconstruction algorithm in integral imaging. Conventional integral imaging has a critical problem that attenuates the visual quality of 3D objects when low-resolution elemental images are used. Although, PERT is one of the solutions, the size of 3D scenes is different from optical reconstruction since it is not considering space between back-projected pixels on reconstruction planes. Therefore, we consider this space and use convolution operator. Especially, convolution operator can be designed by considering aperture shapes. To support our proposed method, we carry out optical experiment and computer simulations.
In this paper, we propose a new high-resolution depth estimation algorithm in integral imaging which can obtain threedimensional (3D) images by using lenslet array. In conventional studies, a stereo-matching is used for depth estimation. However, it is not the best solution for integral imaging since the 3D images are usually low-resolution images. Therefore, we propose a pixel blink rate based algorithm using pixel of the elemental images rearrangement technique (PERT) in integral imaging. Through our optical experiment, the depth resolution by our technique is dramatically improved compared with a conventional method.
In this paper, we propose a new computational reconstruction technique of integral imaging for depth resolution enhancement by using integer-valued and non-uniform shifting pixels. In a general integral imaging system, we can record and visualize (or display) 3D object using lenslet array. In previous studies, many reconstruction techniques such as computational volumetric reconstruction and pixel of elemental images rearrangement technique (PERT) have been reported. However, a conventional computational volumetric reconstruction technique has low visual quality and depth resolution because low resolution elemental images and uniformly distributed shifting pixels are used for reconstruction. On the other hand, our proposed method uses non-uniformly distributed shifting pixels for reconstruction instead of uniformly distributed shifting pixels in conventional computational volumetric reconstruction. Thus, the visual quality and depth resolution may be enhanced. Finally, our experimental results show the improvement of depth resolution and visual quality of the reconstructed 3D images.
In this paper, we propose a new passive image sensing and visualization of 3D objects using concept of both resolution priority integral imaging (RPII) and depth priority integral imaging (DPII) to improve lateral and depth resolutions of 3D images simultaneously. We suppose that elemental images are the most important information for 3D performance of integral imaging, since they include both lateral and depth resolutions of 3D objects. Therefore, all resolutions of the reconstructed 3D images are determined by these elemental images in pickup stage. In this paper, we analyze the lateral and depth resolutions that depend on the basic parameters of camera or lens for pickup. Then, we describe our proposed method. To support our proposed method, we carry out the computer simulation. In addition, we analyze how the surface light of 3D objects placed in arbitrary position can be expressed within the permitted range according to the setting of camera parameters. Finally, to evaluate the performance of our method, peak signal to noise ratio (PSNR) is calculated.