Visual quality enhancement of integral imaging by using pixel rearrangement technique with convolution operator (CPERTS)

In this paper, we propose a new visual quality enhancement of a three-dimensional (3D) computational reconstruction algorithm in integral imaging. Integral imaging can record 3D images easily using a lenslet array. However, the elemental images may have low resolution, because each image cannot use full resolution of an image sensor. To solve this problem, a computational reconstruction technique can be used to reconstruct visual-quality-enhanced 3D images from low-resolution elemental images. Our method is based on the pixel of elemental images rearrangement technique (PERT), which can provide enhanced visual quality of the reconstructed 3D image compared with that of conventional computational reconstruction algorithms. However, it has a problem in which the size of 3D scenes is different from the optical reconstruction results. Therefore, in this paper, we propose a solution considering empty spaces between back-projected pixels on the reconstruction planes and enhance the extensibility using the convolution operator. Our experimental results show the enhancement of the visual quality and resolution of the reconstructed 3D images using the point-spread function filter.