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

Dept. of Visual Content, Dongseo University, Jurye2-Dong, Sasang-Gu, Busan 617-716, Republic of Korea
Optics Communications (Impact Factor: 1.45). 07/2009; 282(14):2760-2767. DOI: 10.1016/j.optcom.2009.04.008


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.

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    • "Thus, a lot of techniques have been discussed in the literature [1]-[16]. Also, image interpolation is still an active research area [4]-[5], [15]-[16]. Among image interpolation methods, the linear (or bilinear) interpolation (LI) and the cubic convolution interpolation [1] (CCI) are the most popular techniques due to their moderate visual quality and low complexity [2]. "
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