Enhanced depth of field and resolution three-dimensional integral imaging by variable spatial filtering and intermediate-view reconstruction technique

In this paper, we propose a novel three-dimensional (3D) integral imaging system to simultaneously improve the depth of field (DOF), resolution, and image quality of reconstructed images by variable spatial filtering and intermediate-view reconstruction technology (IVRT). In the proposed method, the camera performs element images acquisition on a 3D scene with objects of different depths through a 2D grid plane. The reconstructed slice image and block matching algorithm are used to extract the depth of the element images. To improve the sharpness of depth, the Laplace operator is used to perform variable depth filtering on objects of different depths, and depth-enhanced all-filtering element images are obtained through simple pixel fusion. IVRT is applied to all-filtering element images to obtain more element images to reconstruct a resolution-enhanced 3D image. According to the energy of gradient (EOG) value and the Tenengrad value, the reconstruction image quality evaluation of the proposed method is improved by 7.63 and 4.81 times compared with the traditional method, respectively. By the proposed method of generating all-filtering element images and an IVRT in 3D integral imaging system, the experimental results demonstrate that the 3D reconstructed image has extended depth of field, enhanced resolution and improved image quality.