Computational integral imaging reconstruction method of 3D images using pixel-to-pixel mapping and image interpolation
ABSTRACT 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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ABSTRACT: In this paper, an image interpolation method based on even-odd decomposition (EOD) is proposed. An input signal for interpolation is decomposed to even and odd vectors by EOD. And then different interpolation methods are applied to even and odd vectors, respectively. This paper presents an analysis on the two vectors and new design methods for them. Also, based on the new design method, a signal flow graph of the proposed method is provided and compared with the CCI method in terms of complexity. To evaluate the proposed method, we conduct experiments and complexity comparison. The results indicate that the proposed interpolation method does not only outperform the existing method in terms of objective and subjective image quality but also requires less complexity.IEEE Transactions on Consumer Electronics 12/2009; · 1.09 Impact Factor
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ABSTRACT: Integral imaging is an attractive auto-stereoscopic three-dimensional (3D) technology for next-generation 3DTV. But its application is obstructed by poor image quality, huge data volume and high processing complexity. In this paper, a new computational integral imaging (CII) system using multi-view video plus depth (MVD) representation is proposed to solve these problems. The originality of this system lies in three aspects. Firstly, a particular depth-image-based rendering (DIBR) technique is used in encoding process to exploit the inter-view correlation between different sub-images (SIs). Thereafter, the same DIBR method is applied in the display side to interpolate virtual SIs and improve the reconstructed 3D image quality. Finally, a novel parallel group projection (PGP) technique is proposed to simplify the reconstruction process. According to experimental results, the proposed CII system improves compression efficiency and displayed image quality, while reducing calculation complexity.3D Research. 3(4).