Computational Integral Imaging Reconstruction of a Partially Occluded Three-Dimensional Object Using an Image Inpainting Technique

Abstract

In this paper we propose an improved version of the computational integral imaging reconstruction (CIIR) for visualizing a partially occluded object by utilizing an image inpainting technique. In the proposed method the elemental images for a partially occluded three-dimensional (3D) object are recorded through the integral imaging pickup process. Next, the depth of occlusion within the elemental images is estimated using two different CIIR methods, and the weight mask pattern for occlusion is generated. After that, we apply our image inpainting technique to the recorded elemental images to fill in the occluding area with reliable data, using information from neighboring pixels. Finally, the inpainted elemental images for the occluded region are reconstructed using the CIIR process. To verify the validity of the proposed system, we carry out preliminary experiments in which faces are the objects. The experimental results reveal that the proposed system can dramatically improve the quality of a reconstructed CIIR image.

Neighboring Elemental Image Exemplar Based Inpainting for Computational Integral Imaging Reconstruction with Partial Occlusion

“…It is shown experimentally in [20] that the LI-CIIR shows good results for images with objects having smooth surfaces. However, for the case that the object has a textured surface, the LI-CIIR cannot recover the textured data in the data missing region well.…”

“…In the smoothing based inpainting technique the data from the boundary is smoothed into the missing region to fill in the inpainting region. In [20], we proposed an occluding region removal method based on this inpainting technique to fill in the missing data in the occluding region.…”

“…However, even though the occluding object is removed, there remain data missing holes which prevent us from getting better visual qualities of the reconstructed images. Therefore, in [20], a linear inpainting based computational integral image reconstruction (LI-CIIR) method has been proposed, which fills in the data missing region caused by the occluding object by the linear inpainting technique [21] using the information of neighboring pixels.…”

Depth and All-in-Focus Image Estimation in Synthetic Aperture Integral Imaging Under Partial Occlusions

“…In [24] a similar strategy has been used by means of a previous estimation of depths of the edges belonging to the occluding objects, in order to improve the accuracy in the depth map. Others apply methods to fill in the missing information using, for instance, inpainting techniques [31].…”